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2017-1671

Project Title: 
Comparative safety and effectiveness of cognitive enhancers for Alzheimer's dementia: a systematic review and IPD NMA
Specific Aims of the Project: 

The aim of this study is to examine the comparative effectiveness and safety of cognitive enhancers versus placebo or best supportive care by patient characteristics, such as AD severity and sex. We will use IPD-NMA to identify potential treatment effect modifiers, and estimate the most effective and safest treatments for patients with different characteristics. The outputs of our project are to provide clinicians, patients and caregivers with tailored evidence to inform their decision making, improving the health of patients living with AD.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Ongoing
Scientific Abstract: 

Background: Alzheimer's dementia (AD) is the most common cause of dementia, and several organisations, such as the National Institute for Health and Care Excellence, suggest that management of patients with AD should be tailored to their needs. To date, little research has been conducted on the treatment effect in different subgroups of patients with AD.

Objective: To examine the comparative effectiveness and safety of cognitive enhancers for different patient characteristics.

Study design: Systematic review of randomised clinical trials of any duration comparing cognitive enhancers alone or in any combination against other cognitive enhancers, or placebo in adults with AD.

Participants: Adults (aged ≥18 years) diagnosed with AD

Main Outcome Measures: The primary outcome of interest is cognition according to the Mini-mental State Examination (MMSE), and the secondary outcome is overall serious adverse events.

Statistical Analysis: We will perform a Bayesian hierarchical random-effects meta-analysis combining the individual patient data (IPD) from each eligible study. If the identified treatment comparisons form a connected network diagram, we will perform an IPD network meta-analysis (NMA) to estimate subgroup effects for patients with different characteristics, such as AD severity and sex. We will combine aggregated data from studies that we will not be able to obtain IPD, with the IPD provided by the original authors, in a single model. We will use the PRISMA-IPD[1] and PRISMA-NMA[2] statements to report our findings.

Brief Project Background and Statement of Project Significance: 

Alzheimer's dementia (AD) is the most common cause of dementia, and has an insidious onset with progressive deterioration in cognition (eg, memory, thinking and perception), function, behaviour and mood. To date, 46.8 million people worldwide live with dementia. This number will almost double every 20 years, and it is estimated to reach 131.5 million by 2050.[3] A study showed that as age increases, the rates of AD increase overall for both men and women, but it is more prevalent in women (rate/100 years=2.50 (1.85–3.41)) than men (rate/100 years=1.89 (1.22–2.94)).[4] It is currently unclear if galantamine, rivastigmine or donepezil should be used by patients with severe AD, and whether memantine is the most optimal treatment for severe AD.[5] The use of acetylcholinesterase inhibitors and increased doses of donepezil in patients with dementia increase the risk of bradycardia, as well, cholinesterase inhibitors doubles the risk of hospitalisation for bradycardia in older patients.[6, 7] Also, the use of other medications may increase risk of adverse events. For example, cardiac medications like β-blockers may increase risk of bradycardia, and anti-inflammatories may increase risk for gastrointestinal bleeding.[6, 8-10]

To determine the relative effectiveness and safety of cognitive enhancers for patients with different patient characteristics (eg, mild-moderate AD vs severe AD, females vs males), we aim to conduct a systematic review and individual patient data (IPD) network meta-analysis (NMA). In AD, patients may respond differently to the medication based on severity of AD and sex, and hence severity and sex could be considered treatment effect modifiers. The optimal approach to tailor results to the patient characteristics is via using IPD. Tailoring the management of patients with AD is an issue that has been also brought up by several organisations,[11] including the Alzheimer's Society of Ontario[12] and the National Institute for Health and Care Excellence (NICE).[13] Also, the Alzheimer's Disease International (ADI) federation in their world Alzheimer report 2015 mention that there has been dramatically little research into the treatment effect across people of different age and sex.[3]

We previously attempted a systematic review and NMA of aggregated data, but we were unable to provide definitive conclusions regarding the influence of patient characteristics on the results.[14, 15] In this study we tailored results to age, AD severity, comorbidity and study duration via subgroup analysis. These results were similar to 4 Cochrane reviews examining cognitive enhancers for AD.[16-19] The reviews showed that donepezil, rivastigmine and galantamine, significantly improved cognition[16-19] against placebo, yet cholinisterase inhibitors overall and donepezil improved behaviour,[16, 17] cholinisterase inhibitors overall and rivastigmine improved function,[17, 18] and rivastigmine improved AD severity.[18] The use of IPD will increase power and will help explain the relationship between treatment effects and patient-level characteristics.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

We have updated our previous review[15] using the following criteria:
• Population: Adults (aged ≥18 years) with AD diagnosed using various criteria (eg, Diagnostic and Statistical Manual of Mental Disorders, Nursing Minimum Data Set criteria) of any duration with either moderate AD.[20]
• Interventions: Cognitive enhancers (donepezil, rivastigmine, galantamine and memantine) alone or in any combination.
• Comparators: Cognitive enhancers, best supportive care alone or in any combination, and placebo.
• Outcomes: MMSE and overall serious adverse events.
• Study design: We will restrict to RCTs, and will exclude quasi-RCTs.
• Time: Studies of any duration conducted at any time.
• Other: Published studies written in any language will be included.
In case study publications reported data from the same study group (eg, companion reports), we included the most recent study.
Our systematic reviews identified 139 relevant studies. We will include IPD from the studies reported in the supplementary, as well as aggregate data from all remaining published studies.

Narrative Summary: 

Alzheimer’s Dementia (AD) is the most common cause of dementia. Patients living with AD have a lower quality of life (deterioration in memory, thinking, perception, function, behaviour, and mood) and AD ultimately leads to death. Currently, there is no cure for AD, and patients may respond differently to the medication based on their characteristics (eg, severity of disease, sex). We aim to investigate the association between the cognitive enhancers for different patient characteristics and Mini-mental State Examination or overall serious adverse events. The findings of this study will help to improve guidelines for the management of patients with AD.

Project Timeline: 

Our study protocol was published on 7 December 2015 in an open access journal (see http://bmjopen.bmj.com/content/6/1/e010251). We started contacting the study authors to request for their IPD on 10 June 2016. By the time we receive the IPD we will collect, review and clean the data within 3-4 months. We anticipate that the statistical analyses will take another 3-4 months, depending on the complexity of the models and data. We will need approximately 2-3 months to prepare the manuscript and submit it. We expect the first submission of the manuscript will be approximately in March 2018.

Dissemination Plan: 

The findings of our study will fill an important knowledge gap in healthcare, and will be used to inform decision-making for patients suffering from this debilitating disease. The results of this systematic review and IPD-NMA will be of interest to stakeholders, including decision makers, guideline developers, clinicians, methodologists and patients. The dissemination of our findings will be knowledge user-driven and tailored to how and when knowledge users want to receive information. Team members will act as knowledge brokers, using their networks to facilitate dissemination, such as The Cochrane Collaboration, PRISMA-IPD, Drug Safety and Effectiveness Network (DSEN). We will also host a knowledge exchange event with our partners to discuss the results and facilitate dissemination. We will publish our findings in an open access journal, and present them at relevant meetings (Canadian Geriatrics Society; CGS), as well to newsletters of organisations (Alzheimer's Society of Ontario, CGS).

Bibliography: 

1. Stewart LA, Clarke M, Rovers M, et al. Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: the PRISMA-IPD Statement. JAMA. 2015;313(16):1657-65.
2. Hutton B, Salanti G, Caldwell DM, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162(11):777-84.
3. Prince M, Wimo A, Guerchet M, Ali GC, Wu YT, Prina M. World Alzheimer Report 2015. The global impact of dementia. An analysis of prevalence, incidence, cost and trends. London, UK; 2015.
4. Katz MJ, Lipton RB, Hall CB, et al. Age-specific and sex-specific prevalence and incidence of mild cognitive impairment, dementia, and Alzheimer dementia in blacks and whites: a report from the Einstein Aging Study. Alzheimer Dis Assoc Disord. 2012;26(4):335-43.
5. National Institute for Health and Clinical Excellence. Donepezil, galantamine, rivastigmine and memantine for the treatment of Alzheimer's disease. London, UK; 2011.
6. Park-Wyllie LY, Mamdani MM, Li P, Gill SS, Laupacis A, Juurlink DN. Cholinesterase inhibitors and hospitalization for bradycardia: a population-based study. PLoS Med. 2009;6(9):e1000157.
7. Hernandez RK, Farwell W, Cantor MD, Lawler EV. Cholinesterase inhibitors and incidence of bradycardia in patients with dementia in the veterans affairs new England healthcare system. J Am Geriatr Soc. 2009;57(11):1997-2003.
8. Manurung D, Trisnohadi HB. Beta blockers for congestive heart failure. PLoS Med. 2007;39(1):44-8.
9. Gheorghiade M, Colucci WS, Swedberg K. Beta-blockers in chronic heart failure. Circulation. 2003;107(12):1570-5.
10. Pahor M, Guralnik JM, Furberg CD, Carbonin P, Havlik R. Risk of gastrointestinal haemorrhage with calcium antagonists in hypertensive persons over 67 years old. Lancet. 1996;347(9008):1061-5.
11. Sindi S, Mangialasche F, Kivipelto M. Advances in the prevention of Alzheimer's Disease. F1000Prime Rep. 2015;7:50.
12. Williams AP, Peckham A, Rudoler D, Tam T, Watkins J. Formative evaluation of the Alzheimer society of Toronto counselling program. Alzheimer society of Toronto; 2013.
13. National Institute for Health and Clinical Excellence. Dementia: Supporting people with dementia and their carers in health and social care. London, UK; 2006.
14. Tricco AC, Vandervaart S, Soobiah C, et al. Efficacy of cognitive enhancers for Alzheimer's disease: protocol for a systematic review and network meta-analysis. Syst Rev. 2012;1:31.
15. Tricco AC, Ashoor HM, Rios P, et al. Comparative safety and effectiveness of cognitive enhancers for the treatment of Alzheimer’s disease: A rapidly updated systematic review and network meta-analysis Toronto, Canada; 2015.
16. Birks J, Harvey RJ. Donepezil for dementia due to Alzheimer's disease. Cochrane Database Syst Rev. 2006(1):Cd001190.
17. Birks J. Cholinesterase inhibitors for Alzheimer's disease. Cochrane Database Syst Rev. 2006(1):Cd005593.
18. Birks J, Grimley Evans J, Iakovidou V, Tsolaki M, Holt FE. Rivastigmine for Alzheimer's disease. Cochrane Database Syst Rev. 2009(2):Cd001191.
19. Loy C, Schneider L. Galantamine for Alzheimer's disease and mild cognitive impairment. Cochrane Database Syst Rev. 2006(1):Cd001747.
20. Burback D, Molnar FJ, St John P, Man-Son-Hing M. Key methodological features of randomized controlled trials of Alzheimer's disease therapy. Minimal clinically important difference, sample size and trial duration. Dement Geriatr Cogn Disord. 1999;10(6):534-40.
21. Cochrane Effective Practice and Organisation of Care Review Group (EPOC). Data Collection Checklist. Available from: http://methods.cochrane.org/sites/methods.cochrane.org.bias/files/public....
22. Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from www.handbook.cochrane.org.
23. Chaimani A, Higgins JP, Mavridis D, Spyridonos P, Salanti G. Graphical tools for network meta-analysis in STATA. PLoS One. 2013;8(10):e76654.
24. Puhan MA, Schunemann HJ, Murad MH, et al. A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ. 2014;349:g5630.
25. Turner RM, Omar RZ, Yang M, Goldstein H, Thompson SG. A multilevel model framework for meta-analysis of clinical trials with binary outcomes. Stat Med. 2000;19(24):3417-32.
26. Higgins JP, Whitehead A, Turner RM, Omar RZ, Thompson SG. Meta-analysis of continuous outcome data from individual patients. Stat Med. 2001;20(15):2219-41.
27. Sutton AJ, Kendrick D, Coupland CA. Meta-analysis of individual- and aggregate-level data. Stat Med. 2008;27(5):651-69.
28. Higgins JP, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods. 2012;3(2):98-110.
29. White IR, Barrett JK, Jackson D, Higgins JP. Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Res Synth Methods. 2012;3(2):111-25.
30. Song F, Altman DG, Glenny AM, Deeks JJ. Validity of indirect comparison for estimating efficacy of competing interventions: empirical evidence from published meta-analyses. BMJ. 2003;326(7387):472.
31. Veroniki AA, Vasiliadis HS, Higgins JP, Salanti G. Evaluation of inconsistency in networks of interventions. Int J Epidemiol. 2013;42(1):332-45.
32. Donegan S, Williamson P, D'Alessandro U, Garner P, Smith CT. Combining individual patient data and aggregate data in mixed treatment comparison meta-analysis: Individual patient data may be beneficial if only for a subset of trials. Stat Med. 2013;32(6):914-30.
33. Donegan S, Williamson P, D'Alessandro U, Tudur Smith C. Assessing the consistency assumption by exploring treatment by covariate interactions in mixed treatment comparison meta-analysis: individual patient-level covariates versus aggregate trial-level covariates. Stat Med. 2012;31(29):3840-57.
34. Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Series B Stat Methodol. 2002;64(4):583-639.
35. Salanti G, Ades AE, Ioannidis JP. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol. 2011;64(2):163-71.
36. Lambert PC, Sutton AJ, Burton PR, Abrams KR, Jones DR. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Stat Med. 2005;24(15):2401-28.
37. Turner RM, Davey J, Clarke MJ, Thompson SG, Higgins JP. Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews. Int J Epidemiol. 2012;41(3):818-27.
38. Rhodes KM, Turner RM, Higgins JP. Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data. J Clin Epidemiol. 2015;68(1):52-60.
39. Mavridis D, White IR, Higgins JP, Cipriani A, Salanti G. Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta-analysis. Stat Med. 2015;34(5):721-41.
40. Spineli LM, Higgins JP, Cipriani A, Leucht S, Salanti G. Evaluating the impact of imputations for missing participant outcome data in a network meta-analysis. Clin Trials. 2013;10(3):378-88.
41. Lunn D, Spiegelhalter D, Thomas A, Best N. The BUGS project: Evolution, critique and future directions. Stat Med. 2009;28(25):3049-67.

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
New research question to examine treatment safety
Participant-level data meta-analysis:
Participant-level data meta-analysis will pool data from YODA Project with other additional data sources
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The primary outcome of interest is cognition according to the MMSE (efficacy outcome, continuous variable), and the secondary outcome is overall serious adverse events (SAEs; safety outcome, dichotomous variable); both outcomes were reported by many of the included trials previously and for which NMA was possible. In particular, in our previous NMA using aggregated data, 60 RCTs with 15 862 patients contributed to a NMA for the MMSE outcome, and 51 RCTs with 19 329 patients contributed to a NMA for SAEs.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

We will use a data-driven approach. More specifically, all IPD variables provided will be entered in our NMA and in the meta-regression analysis we will start by including one dependent and one independent variable. Then significant moderators will simultaneously be entered into multiple regression models as long as the minimum number of cases per independent variable is 10. Our goal is to avoid over-fitting and provide reliable treatment effect estimates.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

The data we plan to abstract include study characteristics (eg, year of publication), aggregated patient characteristics (eg, number of patients), outcome results (eg, MMSE, SAE) and source of funding (categorised as: funded/authored by an employee of a drug manufacturer or other commercial organisation, government-sponsored/non-profit organisations, including universities and hospitals, no funding, funding unclearly reported, and funding not reported).[21] Two reviewers will abstract data independently, and all conflicts will be resolved through discussion. The year of publication and funding are potential effect modifiers. Therefore, these factors will be explored in a network meta-regression assuming a common fixed coefficient across treatment comparisons.

Statistical Analysis Plan: 

As with the original review, we will appraise the risk of bias using the Cochrane Risk of Bias tool.[22] We will draw a comparison-adjusted funnel plot[23] for both outcomes. Two review authors will also independently assess the quality of evidence in each NMA using the GRADE approach as extended for network meta-analysis.[24]

We will perform a Bayesian hierarchical random-effects meta-analysis for each treatment comparison, as we anticipate clinical and methodological between-study heterogeneity. We will perform a two-stage analysis, where at the first stage each individual will be analysed separately in each trial and at the second step the trial parameter estimates will be synthesised in a pairwise meta-analysis. All IPD from included studies will be first aggregated to study-level summary statistics using the R software (platform provided by the YODA project), and then these estimates will be introduced into the random-effects meta-analysis model. We will use the odds ratio for SAE[25] and the mean difference effect size for MMSE.[26] In case we are able to obtain IPD for a subset of trials, then we will use a two-part model with the same between-study variance in both parts and accounting for treatment-by-covariate interactions (including for example co-morbidities such as arrhythmias in the model[27]). The first part will entail the two-stage model described above using IPD only, whereas the second part will entail applying a pairwise meta-analysis with aggregate data.[27]
For a connected network of trials, the random-effects NMA model will be used. If possible, we will combine information across a network of trials using only IPD. If we are not successful in obtaining IPD for at least one study, we will combine both IPD and aggregated data in a single model. Again, a two-part analysis will be applied, considering the IPD reduced to aggregate data in the first part, and the aggregate data as identified in the published trials in the second part. Both IPD and aggregate data studies will share the same amount of heterogeneity. Information on patient-level covariates (eg, AD severity, sex) will be included in the model as secondary analyses. We will evaluate the consistency assumption using the design-by-treatment interaction model [28, 29] and the loop-specific method [30, 31] using aggregated data. If inconsistency is suggested, we will check the data for discrepancies and if none are identified, subgroup or meta-regression analyses will be performed.
We will estimate subgroup effects (eg, age, sex) using treatment-by-covariate interaction terms within studies and combining these across studies. We will apply 3 model specifications assuming that the regression coefficients are: a) different and unrelated across comparisons, b) different but related, sharing the same distribution, and c) identical across comparisons.[32, 33] We will compare the results of the models by evaluating the statistical significance of the regression coefficients for interactions, monitoring the reduction in the between-study variance, and using the Deviance Information Criterion[34] to compare the overall fit and parsimony of the models. We will rank the interventions for each outcome using the surface under the cumulative ranking curve.[35]

We will conduct multiple sensitivity analyses to examine the robustness of our results. We will: 1) restrict to studies with IPD only, 2) use different priors for the between-study variance, [36-38]3) restrict to RCTs with a low risk of bias, 4) use different imputation techniques for missing outcome data.[39, 40]

All pairwise meta-analyses and NMAs will be conducted using the Bayesian software OpenBUGS.[41] Two chains will be generated and convergence will be evaluated by their mixing, after discarding the first 10,000 iterations. We will use vague priors for all parameters of the models apart from the between-study variance for which we will use informative priors.[37, 38]

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/426">NCT00253227 - GAL-INT-2 - Galantamine in the Treatment of Alzheimer's Disease: Flexible Dose Range Trial</a></li><li><a href="/node/1043">NCT00679627 - GALALZ3005 - A Randomized, Double-Blind, Placebo-controlled Trial of Long-term (2-year) Treatment of Galantamine in Mild to Moderately-Severe Alzheimer's Disease</a></li><li><a href="/node/1556">NCT00216593 - GAL-ALZ-302 (PMID # 19042161-CR003940) - Treatment of Severe Alzheimer's Disease in a Residential Home, Nursing Home, or Geriatric Residential Setting: Evaluation of Efficacy and Safety of Galantamine Hydrobromide in a Randomised, Doubleblind, Placebo-Controlled Study</a></li><li><a href="/node/2371">GAL-93-01 - A group comparative, placebo-controlled, double-blind trial of the efficacy and safety of galantamine hydrobromide, 7.5 mg (6 mg galantamine base) TID, 10 mg (8 mg galantamine base) TID and 15 mg (12 mg galantamine base) TID taken orally for 12 weeks in patients with a diagnosis of senile dementia of the Alzheimer’s type</a></li><li><a href="/node/2696">GAL-USA-10 - Placebo-controlled evaluation of galantamine in the treatment of Alzheimer’s disease: Evaluation of safety and efficacy under a slow titration regimen</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2018

2017-1456

Project Title: 
The efficacy of biologic medications in improving depressive symptoms in patients with PsA – Patient-level meta-analysis
Specific Aims of the Project: 

We propose an individual-level meta-analysis using data from clinical trials among patients with PsA. We aim to examine how depressive mood changes with therapy intervention and the time course over which it changes. More specifically, we will compare the efficacy of the following two biologic medications for PsA: golimumab (TNF inhibitor) and ustekinumab (IL-12/IL-23 inhibitor) in improving depressive symptoms among patients with PsA participating in clinical trials.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Ongoing
Scientific Abstract: 

Background: Psoriatic arthritis (PsA) is frequently associated with other co-morbidities including depression. Systemic inflammation is associated with depression, thus suppression of inflammation may have a beneficial effect on depressive symptoms.

Objective: To compare the efficacy of TNF inhibitor and IL-12/IL-23 inhibitor in improving depressive symptoms among patients with PsA participating in clinical trials.

Study Design: We will conduct a patient level meta-analysis of data from randomized placebo-control clinical trials assessing the efficacy of the following medications in patients with PsA: golimumab (TNF inhibitor) and ustekinumab (IL-12/IIndividual-patient data from each trial will be merged into a single dataset using common variables including demographics, co-morbidities including depression, measures of psl-23 inhibitor).

Participants: Patients with active PsA from clinical trials.

Main Outcome Measures: The primary outcome of the study is the change in depressive symptoms in study drug arm compared with the placebo arm. The Short-Form Health Survey (SF-36) mental component summary (MCS) score will be used assess the change in depressive symptoms over time.

Statistical Analysis:
Analyses will be conducted in the randomized set including all patients randomized to the study with complete data. The study outcome will be reported at all time points during the double-blind portion of the by exposure to the study drug or placebo.
The effect of each drug on depressive symptoms will be assessed using multivariable regression analysis.

Brief Project Background and Statement of Project Significance: 

Depression has wide reaching affects; it has been associated with an increased risk for cardiovascular disease, weight gain, increased musculoskeletal pain, and poor quality of life in the general population1, 2. These factors can have a significant impact on the course of other diseases including psoriasis and psoriatic arthritis (PsA). Psoriasis is common inflammatory skin disorder affecting 2-4% of the population3, 4. PsA is a chronic and often debilitating inflammatory arthritis affecting nearly one third of patients with psoriasis5, 6. Psoriatic disease (PsD), including both psoriasis and PsA, is associated with a number of comorbidities. Depression is among the most impactful on quality of life but has been given little attention in the literature7. However, recently, the awareness of this issue was heightened by concerns regarding drug-induced depression among patients with PsD participating in clinical trials. These events have underscored the importance of better understanding the relationship between depression and PsD and the impact of therapy for PsD on depression.
Depression is common among patients with PsA. The association between PsD and depression has been acknowledged for many years in the dermatology literature5, 6, 8. Patients with PsD may have debilitating joint disease with pain, fatigue, reduced ability to work, and embarrassment about joint deformities and skin disease. All of these factors can lead to depression. However, depression may also be related to the pathophysiology of PsD rather than soley a result of the clinical manifestations of PsD. In fact, depression may precede disease symptoms, particularly for PsA. Patients with psoriasis who were depressed were more likely to develop PsA than patients without depression9.
Inflammation and Depression. While depression has been primary thought of as a disregulation of neurotransmitters, recent evidence suggests that there are multiple processes at play, part of which is immune activation and inflammation. Previous meta-analyses have found that pro-inflammatory cytokines are associated with major depression. There is an overlap between some of the pro-inflamamtory mechnisms that have been linked with depression and PsD10,11. Thus, therapy for PsD may also have implications for depression. There is little data about the anti-depressant effects of biologic medications in humans10.
Significance: Despite this potential direct connection between the two disorders and the important clinical implications, little is known about the impact of effective control of inflammation on depressive mood in patients with psoriasis and PsA and the direct impact of therapies for PsD on depression. Furthermore, certain drugs may not be advantageous for patients with PsD and depression. Currently, there is no data about the relative efficacy of the various biologic medications in improving depressive symptoms in patients with psoriasis and PsA. The goal of this application is to examine how depression changes with treatment of PsD.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

We will conduct a patient level meta-analysis of data from phase 2 and 3 randomized double-blinded placebo-control clinical trials assessing the efficacy of the following classes of medications in patients with psoriatic arthritis (PsA): 1) TNF inhibitors (golimumab); 2) IL-23/IL-12 inhibitor (ustekinumab);
The study population will include patients with active PsA that were treated with one of the 2 classes of biologic medications indicated above.

Inclusion criteria
1) Randomized placebo-control clinical trial in patients with active PsA
2) Phase 2 or 3 trials

Exclusion criteria
1) Open label study/extension period of RCTs
2) Phase 1 studies
3) Lack of SF-36 questionnaire response

For each trial the analysis will include all patients who were randomized to receive the study drug or placebo. Patients in the study drug arm will be compared to patients in the placebo arm. The comparison will include information collected during the double-blind portion of the trial. Information from the extension period of the trial will not be included in the analysis.

Narrative Summary: 

In this study we aim to compare the effect of three classes of medications used for the treatment of psoriasis and psoriatic arthritis in improving depressive symptoms in patients participating in clinical trials. The three classes of medications include: TNF inhibitors, IL-12/IL-23 inhibitors and IL-17 inhibitors.
We will combine data from clinical trials that assessed the effect of the above mentioned drugs. The study outcome will be the change in depressive symptoms that will be measured by a component of a quality of life questionnaire.
The results of the study will assist physicians treating patients with PsD in selecting the appropriate class of medication.

Project Timeline: 

The project will be completed over a period of 12 months. We have already obtained an exemption from ethics approval form the Women’s College Hospital REB (REB # 2017-0007-E Research Ethics Exemption Letter). The expected timeline:
March 2017 - Submit Requests for Data
December 2017 – completion of data analysis
March 2018 – Finish drafting the manuscript and submission for publication

Dissemination Plan: 

Knowledge dissemination strategies of our results will include a peer-reviewed and presentations at local and national and international medical conferences. We will also partner with patient organizations to disseminate the results of our study to the psoriasis and arthritis communities through publication in their websites (e.g., Arthritis Alliance of Canada, Arthritis Society, Canadian Association of Psoriasis Patients).

Bibliography: 

References
1. Iaquinta M, McCrone S. An Integrative Review of Correlates and Predictors of Depression in Patients with Rheumatoid Arthritis. Arch Psychiatr Nurs 2015;29(5):265-78.
2. Panagioti M, Scott C, Blakemore A, Coventry PA. Overview of the prevalence, impact, and management of depression and anxiety in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2014;13(9):1289-306.
3. Michalek IM, Loring B, John SM, Takeshita J, Gelfand JM, Li P, Pinto L, Yu X, Rao P, Viswanathan HN, Doshi JA. A systematic review of worldwide epidemiology of psoriasis. LID - 10.1111/jdv.13854 [doi]
Psoriasis in the US Medicare Population: Prevalence, Treatment, and Factors Associated with Biologic Use. (1468-3083 (Electronic)).
4. Takeshita J, Gelfand JM, Li P, Pinto L, Yu X, Rao P, Viswanathan HN, Doshi JA. Psoriasis in the US Medicare Population: Prevalence, Treatment, and Factors Associated with Biologic Use. J Invest Dermatol 2015:[Epub ahead of print].
5. Dommasch ED, Li T, Okereke OI, Li Y, Qureshi AA, Cho E. Risk of depression in women with psoriasis: a cohort study. Br J Dermatol 2015:[Epub ahead of print].
6. McDonough E, Ayearst R, Eder L, Chandran V, Rosen CF, Thavaneswaran A, Gladman DD. Depression and anxiety in psoriatic disease: prevalence and associated factors. J Rheumatol 2014;41(5):887-896.
7. Ogdie A, Schwartzman S, Husni ME. Recognizing and managing comorbidities in psoriatic arthritis. Curr Opin Rheumatol 2015;27(2):118-26.
8. Kotsis K, Voulgari PV, Tsifetaki N, Machado MO, Carvalho AF, Creed F, Drosos AA, Hyphantis T. Anxiety and depressive symptoms and illness perceptions in psoriatic arthritis and associations with physicalhealth-related quality of life. Arthritis Care Res (Hoboken) 2012;64(10):1593-1601.
9. Lewinson R, Vallerand I, Lowerison M, Parsons L, Frolkis A, Kaplan G, Bulloch A, Patten S, Barnabe C. Depression and the Risk of Psoriatic Arthritis Among Patients with Psoriasis: A Population-Based Cohort Study [abstract 2164]. Arthritis Rheumatol 2016;68(Suppl 10).
10. Slyepchenko A, Maes M, Kohler CA, Anderson G, Quevedo J, Alves GS, Berk M, Fernandes BS, Carvalho AF. T helper 17 cells may drive neuroprogression in major depressive disorder: Proposal of an integrative model. Neuroscience and biobehavioral reviews 2016;64:83-100.
11. Barnas JL, Ritchlin CT. Etiology and Pathogenesis of Psoriatic Arthritis. Rheumatic diseases clinics of North America 2015;41(4):643-63.
12. Matcham F, Norton S, Steer S, Hotopf M. Usefulness of the SF-36 Health Survey in screening for depressive and anxiety disorders in rheumatoid arthritis. BMC musculoskeletal disorders 2016;17:224.

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Participant-level data meta-analysis:
Participant-level data meta-analysis uses only data from YODA Project
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The primary outcome of the study is the change in depressive symptoms in study drug arm compared with the placebo arm. The Short-Form Health Survey (SF-36) mental component summary (MCS) score will be used assess the change in depressive symptoms over time.
The gold standard for assessment of depression is a psychiatric evaluation, however, various questionnaires were found to detect depression with reasonable sensitivity and specificity. The SF-36 is a generic measure of quality of life that is commonly measured in clinical trials to assess quality of life. A recent study found that the SF-36 MCS with a threshold of ≤38 could be used to detect major depressive disorder in rheumatoid arthritis patients with a sensitivity of 87%, sensitivity of 80% and accuracy of 83%.12
A secondary analysis will use the SF-36 MCS to classify patients based on depression status. We will use a cut-off of SF-36 MCS≤38 to identify patients with depression. We will perform sensitivity analyses with cut-off points of MCS≤35 and ≤40 as alternative definitions of depression. We will assess the change in depression status during t

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

The primary predictor of the study will be the use of a biologic medication. The reference group will be the use of placebo. For each trial the analysis will include all patients who were randomized to receive the study drug or placebo. Patients in the study drug arm will be compared to patients in the placebo arm. The comparison will include information collected during the double-blind portion of the trial. Information from the extension period of the trial will not be included in the analysis.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

Individual patient data from each trial will be merged into a single dataset using common variables including demographics (age, sex), co-morbidities including depression, disease duration, measures of psoriasis and PsA disease activity including tender and swollen joint counts, dactylitis, enthesitis and psoriasis area and severity index, laboratory inflammatory markers including ESR and CRP, use of study drug and its dose or placebo, concomitant medications including disease modifying anti rheumatic drugs, non-steroidal anti-inflammatory medications, corticosteroids and anti-depressive medications and patient reported outcomes including SF-36 (physical and mental component scores), Health Assessment Questionnaire, patient global assessment of disease activity and pain scores.

Statistical Analysis Plan: 

We will perform the analysis in the combined dataset that will include all studies with complete individual-patient level data.
Analyses will be conducted in the randomized set including all patients randomized to the study with complete data. The study outcome will be reported at all time points during the double-blind portion of the study by exposure to the study drug or placebo.
Initially, each trial will be analyzed separately. Subsequently, the effect of each drug will be assessed individually and finally the effect of each drug class (TNFi or IL-12/IL-23) on depression will be assessed.
We will descriptively report the mean change in SF-36 MCS scores by study arm. Univariate linear regression models will be used to assess the effect of each study drug/drug class on the change in SF-36 MCS scores at the two follow up time points. Each model will also include a study indicator to account for population differences across studies. To assess what is the impact of baseline depression status on the effect of study drug/drug class on the change in Sf-36-MCS, a subgroup analysis by baseline depression status (patients meeting the criteria for depression based on SF-36-MCS cut-off) will be conducted. Subsequently, multivariable regression analyses will be performed to assess the effect of the study drug/drug class on the change in SF-36-MCS after adjusting for the following variables: age, sex, BMI, concomitant disease modifying anti-rheumatic drugs, previous biologic treatment, number of tender and swollen joints, minimal disease activity status, physician global assessment and PASI.
In addition we will consider depressive status as a categorical outcome by classifying patients to Depression or no-Depression based on a cut off of SF-36 MCS≤38.
We will descriptively report the number of patients meeting the criteria for depression by study arm and their demographic characteristics. We will next descriptively report the proportion of patients with depression at baseline who no longer meet criteria for depression at the two follow up time points. Similarly, we will report the number of patients without depression at baseline that become depressed in each group. We will use logistic regression models with depression status as the outcome and study drug/drug class as the primary predictor and baseline depression status as a model co-variate to assess the impact of the primary predictor on transition from baseline depression status to no meeting depression criteria at the two follow-up points. As described above, multivariable regression analyses will also be performed using the co-variates outlined above.
One of the advantages of the trials is that most include more than one dose of the study drug allowing for examination of dose effect. We will initially analyze each drug dose separately. We will assess whether a dose-response effect on depressive symptoms exists by comparing the effect sizes from the regression models across the different drug doses. We will then combine the different doses to a single group.
Sensitivity analyses: In studies in which depression indicators, either patient-reported depression, physician-reported depression or depression indices are included, we will report the results using both definitions of depression.
In studies with <10% missing data, can use multiple imputation to see if results are different (or use this as primary. A complete case analysis will be conducted. We will examine whether there are differences at baseline among those with and without missing data.
The primary analysis will be performed according to the intention to treat principle with the outcome measure assessed at the first escape point (approximately 12 to 16 weeks). A secondary analysis will assess the outcome at the second follow up period (24 weeks). A sensitivity analysis will be performed per-protocol at each follow up time point.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/557">NCT00265096 - C0524T08 - A Multicenter, Randomized, Double-blind, Placebo controlled Trial of Golimumab, a Fully Human Anti-TNFa Monoclonal Antibody, Administered Subcutaneously in Subjects with Active Psoriatic Arthritis</a></li><li><a href="/node/558">NCT01009086 - CNTO1275PSA3001 /// PSUMMIT I - A Study of the Safety and Effectiveness of Ustekinumab in Patients With Psoriatic Arthritis</a></li><li><a href="/node/559">NCT01077362 - CNTO1275PSA3002 /// PSUMMIT II - A Study of the Safety and Efficacy of Ustekinumab in Patients With Psoriatic Arthritis With and Without Prior Exposure to Anti-TNF Agents</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017

2017-1366

Project Title: 
Comparative efficacy and tolerability of pharmacological interventions for attention-deficit/hyperactivity disorder in children, adolescents and adult
Specific Aims of the Project: 

To rank pharmacological treatments in patients with ADHD, in terms of:
1. Overall efficacy on ADHD core symptoms;
2. Tolerability;
3. Global functioning;
4. Acceptability;
5. Changes in blood pressure and weight.

What type of data are you looking for?: 
Full CSR

Application Status

Incomplete Not Reviewed
Scientific Abstract: 

Currently, there is a lack of up-to-date and comprehensive evidence on how available ADHD drugs compare and rank in terms of efficacy and tolerability, in children or adolescents as well as in adults. We will conduct a network meta-analysis (NMA), integrating direct and indirect comparisons from randomised controlled trials (RCTs), to rank pharmacological treatments for ADHD according to their efficacy and tolerability profiles. We will search a broad range of electronic databases, including PubMed, MEDLINE, EMBASE, PsycINFO, ERIC, and Web of Science, with no date or language restrictions. We will also search for unpublished studies using international clinical trial registries and contacting relevant drug companies. We will identify and include available parallel-group, cross-over and cluster randomised trials that compare methylphenidate, dexmethylphenidate, amphetamine derivatives, lisdexamfetamine, atomoxetine, clonidine, guanfacine, bupropion, or modafinil (as oral therapy) either with each other or to placebo, in children, adolescents or adults with ADHD. Primary outcomes will be efficacy (indicated by reduction in severity of ADHD core symptoms measured on a standardised scale) and tolerability (the proportion of patients who left a study early due to side effects). Secondary outcomes will be global functioning, acceptability (proportion of patients who left the study early by any cause), and changes in blood pressure and body weight. NMA will be conducted in STATA within a frequentist framework. Quality of RCTs will be evaluated using the Cochrane risk of bias tool.

Brief Project Background and Statement of Project Significance: 

This is the first comprehensive network meta-analysis (NMA) addressing the efficacy and tolerability of medications for attention-deficit/ hyperactivity disorder (ADHD) in children, adolescents and adults. By integrating direct and indirect evidence from all included studies, it will increase the precision of treatment estimates and allow for the ranking of available ADHD drugs in terms of efficacy and acceptability.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

Studies that include children (≥5 and ≤12 yrs), adolescents(>12 and <18 yrs) and/or adults (≥18 yrs) who: (1) meetDSM-III, II-R, IV, IV-TR or 5 criteria for a primary diagnosisof ADHD or ICD-10 criteria for a primary diagnosisof HKD (we will not include studies using DSM-IIcriteria since they did not use standardised criteria).No restriction on ADHD subtype/presentation,gender, IQ or socioeconomic status of participants.Some, or all, participants could have one or more psychiatric or neurological comorbidities (except genetic syndromes), unless they were pharmacologically treated during the study for these comorbidities.
Exclusion: studies recruiting patients with a diagnosis of Minimal Brain Dysfunction, trials in which ADHD is a comorbid disorder secondary to a genetic syndrome; studies enrolling participants defined as ‘hyperkinetic’ or ‘hyperactive’ defined without standardised diagnostic criteria, patients who were
taking ADHD medication prior to entering the study, unless appropriate wash out done, participants who previously responded to the same medication tested or were responders or stabilised to an ADHD medication, participants ‘resistants’ to previous ADHD med

Narrative Summary: 

We will conduct a network metaanalysis (NMA), integrating direct and indirect comparisons from randomised controlled trials (RCTs), to rank pharmacological treatments for ADHD according to their efficacy and tolerability profiles.

Project Timeline: 

Data extraction from the included studies is complete and requests for data that are missing in the published / available reports are being sent to authors and sponsors. Data freeze is planned for the end of February 2017.

Dissemination Plan: 

Results from this study will be published in a peer-reviewed journal and possibly presented at relevant national and
international conferences.

Bibliography: 

Turner RM, Davey J, Clarke MJ, et al. Predicting the extent of
heterogeneity in meta-analysis, using empirical data from the
Cochrane Database of Systematic Reviews. Int J Epidemiol
2012;41:818–27.

What is the purpose of the analysis being proposed? Please select all that apply.: 
Summary-level data meta-analysis:
Summary-level data meta-analysis will pool data from YODA Project with other additional data sources
Supplementary Material: 
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Full details of the outcomes of interest can be found in our published protocol here: https://www.ncbi.nlm.nih.gov/pubmed/28073796.
Efficacy (as continuous outcome) on severity of ADHD core symptoms (total combined, ie, inattentive plus hyperactive/impulsive symptoms), measured as
endpoint score on a standardised scale filled out by parents, teachers, patients or clinician(s). Selected scales: ADHD Rating Scale (total score), SNAP ADHD (total score), Conners rating scale (any version, ADHD total score) or other ADHD scales.
Tolerability of treatment, defined as the proportion of patients who left the study early due to any side effects during the first 12 weeks of treatment.
Global functioning, measured by the Clinical Global Impressions-Improvement (CGI-I, investigator’s rating).
Acceptability of treatment, defined as the proportion of patients who left the study early for any reason during the first 12 weeks of treatment.
Change in blood pressure (diastolic and systolic), measured in mm Hg.
Change in body weight, measured in kg.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

Treatment with any of the following drugs, as oral monotherapy (tablets, capsules, chewable compounds or liquid formulations), compared with
each other or with placebo: methylphenidate, dexmethylphenidate, atomoxetine, amphetamine derivatives (including lisdexamfetamine), clonidine, guanfacine,
bupropion and modafinil. For full details details please refer to out research protocol here: https://www.ncbi.nlm.nih.gov/pubmed/28073796.

Statistical Analysis Plan: 

In standard pairwise meta-analyses, we will estimate different heterogeneity variances for each pairwise comparison. In NMA, we will assume a common estimate for the heterogeneity variance (τ2) within and across comparisons. The presence of statistical heterogeneity within each pairwise comparison will be assessed by visual inspection of the forest plots and by calculating the I2 statistic and its confidence limits. The assessment of statistical heterogeneity in the entire network will be based on the magnitude of the common τ2 estimated from the NMA models. For dichotomous outcomes, the magnitude of the heterogeneity variance will be compared with the empirical distribution as derived by Turner et al. Cochrane Database of Systematic Reviews. Int J Epidemiol
2012;41:818–27.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/1082">NCT01323192 - JNS001-JPN-A01 - A Double-blind, Placebo-controlled, Parallel-Group Study to Evaluate the Efficacy and Safety of JNS001 in Adults With Attention-Deficit/Hyperactivity Disorder at Doses of 18 mg, 36 mg, 54 mg, or 72 mg Per Day</a></li></ol>
Make Publicly Available : 

2017-1356

Project Title: 
Does Body Mass Index predict efficacy of abiraterone acetate therapy in patients with metastatic castration-resistant prostate cancer?
Specific Aims of the Project: 

The objective of this study is to use data from a large clinical trial of drug therapy in men with metastatic prostate cancer to identify whether BMI could act as a predictor of the efficacy of abiraterone acetate reffering to progression-free survival and overall survival.
Hypothesis: We supposed that excess body weight, as measured by BMI, may lead to the treatment resistance to the abiraterone of prostate cancer.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Ongoing
Scientific Abstract: 

Background: The increase in prostate cancer incidence and mortality observed in immigrants from low-risk to high-risk countries suggests that lifestyle and dietary factors play an important role in the etiology of prostate cancer. Excess body weight comprehensively reflects lifestyle and dietary factors, which occurs when the expenditure (i.e., physical activity) is less than the intake (i.e., high-fat diets).[1-3] Excess body weight, as measured by BMI, has been considered a factor for decreased and increased risk of localized and advanced prostate cancer, respectively. However, the relationship between BMI and efficacy of abiraterone acetate therapy remains unclear.
Objective: The objective of this study is to use data from a large clinical trial of drug therapy in men with metastatic prostate cancer to identify whether BMI could provide some indication of efficacy of abiraterone acetate.
Study Design: Retrospective cohort study.
Participants: mCRPC patients from COU-AA-302 and COU-AA-301 treated with abiraterone or placebo.
Main Outcome Measures(s): Outcomes evaluated will include PSA progression-free survival, overall survival, progression free survival as well as response to subsequent therapies.
Statistical Analysis: Cox regression analysis will evaluate the role of BMI as a prognostic biomarker. Analyses will be stratified by treatment received, ECOG status, LDH, hemoglobin level, Gleason score, TNM stage and age, et al.

Brief Project Background and Statement of Project Significance: 

Excess body weight, as measured by BMI, has been considered a factor for decreased and increased risk of advanced prostate cancer. There is a complex array of biological mechanisms through which obesity may influence prostate carcinogenesis and metastasis, including hyperinsulinemia, elevated insulin-like growth factor (IGF) hormone levels, dysregulation of sex steroid hormones, altered levels of adipokines, and chronic inflammation.[4-6] Obesity is also associated with chronic inflammation and biomarkers of inflammation in the body, such as higher levels of C-reactive protein, which have been associated with prostate cancer–specific mortality. [7,8] Obese men have been shown to exhibit reduced levels of androgens, and there is evidence that men with lower levels of testosterone have more aggressive tumors at clinical presentation.
Abiraterone functions by interference with steroid metabolism . Normally in the adrenal glands, adrenocorticotropic hormone (ACTH) stimulates metabolism of the steroid precursor pregnenolone. Pregnenolone can be further metabolized to aldosterone or to 17OH-pregnenolone, a common precursor for cortisol and testosterone. The action of 17[alpha]-hydroxylase converts pregnenolone to 17OH-pregnenolone, and 17,20-lyase further converts this product to dehydroepiandrostenedione (DHEA). DHEA is subsequently converted to an intermediary and finally testosterone. Abiraterone is a potent inhibitor of the 17[alpha]-hydroxylase and 17,20-lyase enzymatic functions of CYP17.[9] Recent preclinical work has also identified [DELTA]4-abiraterone, an active metabolite of abiraterone, that further inhibits 3[beta]-hydroxy steroid dehydrogenase, CYP17A1, and 5[alpha]-reductase. In the presence of ACTH stimulation and abiraterone, pregnenolone is shunted to mineralocorticoid synthesis. Abiraterone used without replacement corticosteroids to suppress ACTH results in a syndrome of mineralocorticoid excess.[10] Abiraterone thus was studied in conjunction with corticosteroids in its clinical development.
As a result, we supposed that excess body weight, as measured by BMI, may lead to the treatment resistance to the abiraterone of prostate cancer.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

Data source: COU-AA-302 and COU-AA-301
Inclusion criteria: all patients in the trial
Exclusion criteria: missing data

Narrative Summary: 

Recent studies have found that risk varies by stage of disease, tumor grade, and cause-specific mortality. Several meta-analyses have indicated that greater body mass index (BMI) is associated with increased risks of aggressive/advanced prostate cancer and prostate cancer– specific mortality, but the relationships for BMI and efficacy of abiraterone acetate therapy remain inconclusive. The aim of this study is to use data from a large clinical trial of drug therapy in men with metastatic prostate cancer to identify the relationship between BMI and efficacy of abiraterone acetate. The results may help establish an economical and accessible biomarker to predict the efficacy of abiraterone.

Project Timeline: 

Project start date: 3/2017
Analysis completion date: 4/2017
Date manuscript drafted/submitted: 5/2017
Results reported 8/2017

Dissemination Plan: 

We plan to publish the results of this project in the form of a manuscript in oncology and urology medical journals.

Bibliography: 

1. Cao Y, Ma J. Body Mass Index, Prostate cancer–specific mortality, and biochemical recurrence: A systematic review and meta-analysis. Cancer Prev Res.
2011;4(4):486–501.
2. Zhong S, Yan X, Wu Y, et al. Body mass index and mortality in prostate cancer patients: A dose-response meta-analysis. Prostate Cancer Prostatic Dis.
2016;19(2):122–31.
3. Chen Q, Chen T, Shi W, et al. Adult weight gain and risk of prostate cancer: A dose-response meta-analysis of observational studies. Int J Cancer. 2016;
138(4):866–874.
4. Renehan AG, Zwahlen M, Egger M. Adiposity and cancer risk: New mechanistic insights from epidemiology. Nature Reviews Cancer. 2015;15(8):484–498.
5. Hsing AW, Gao YT, Chua S, et al. Insulin resistance and prostate cancer risk.
J Natl Cancer Inst. 2003;95(1):67–71.
6. Albanes D, Weinstein SJ, Wright ME, et al. Serum insulin, glucose, indices of
insulin resistance, and risk of prostate cancer. J Natl Cancer Inst. 2009;101(18):
1272–1279
7. De Marzo AM, Platz EA, Sutcliffe S, et al. Inflammation in prostate carcinogenesis. Nat Rev Cancer. 2007;7(4):256–269.
8. Platz EA, De Marzo AM. Epidemiology of inflammation and prostate cancer.
J Urol. 2004;171(2 Pt 2):S36–S40
9. Sternberg CN, Petrylak DP, Madan RA, et al. Progress in the treatment of advanced prostate cancer. Am Soc Clin Oncol Educ Book 2014:117-31.
10. Attard G, Reid AH, A’Hern R, et al. Selective inhibition of CYP17 with abiraterone acetate is highly active in the treatment of castration-resistant prostate cancer. J Clin Oncol 2009;27:3742-8.

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Date of death (overall survival)
Date of PSA progression (PSA progression-free survival)
Date of Radiographic PFS (Radiographic progression-free survival)

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

Body Mass Index (continuous)

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

In this study, we will not only focus on one predictor. We seek to investigate the variables associated with all cause mortality and disease progression. The variables of interest include:
Age at study entry (continuous)
Race
Height (continuous)
Treatment Arm (Abiraterone or placebo)
Gleason Score (Categorized)
Date of Diagnosis
Presence of liver metastases (Present/Absent)
Presence of bone metastases (Present/absent)
Presence of nodal metastases (Present/absent)
Presence of visceral metastases (Present/absent)
Time from start of initial LHRH to abiraterone treatment (continuous)
Weight (kg, each visit record from inclusion to the end of follow-up)
Prior anti-cancer therapies (number of prior hormonal therapies, prior ketoconazole, prior chemotherapies(COU-AA-302))
Prior prostatectomy and/or radiation therapy (Y/N for each)
Investigations (PSA, Hgb, Cr, AlkPhos, LDH)
Pain score / presence of pain (binary Y/N)
Performance Status (ECOG)
Mode of progression (clinical, radiographic, toxicity)
Best PSA response (% reduction)
Date of Abiraterone or Prednisone initiation
Adverse events or complications occurred during the treatment

Statistical Analysis Plan: 

Descriptive statistics will assess median BMI values at baseline, and at each time of visit during subsequent follow up for placebo or AA treatments. Baseline BMI (< or > 25 or alternate cut-off) values will be compared for differences in known baseline prognostic factors such as LDH, Hgb, AlkPhos, ECOG, pain status, presence of metastases and PSA, et al. Univariate and multivariate cox regression analyses will evaluate the HR of baseline and increases or decreases (based on linear regression of changes over time) in BMI values on outcomes of OS, PFS and response to subsequent therapies. This will be performed separately for both arms of the trial based on treatment received. Area-under-the curve analyses will compare the relative predictive ability of BMI to predict response to AA as measured by best PSA response. All statistical tests will be done using R statistics package, version 2.8.1(http://www.r-project.org/).

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/304">NCT00638690 - COU-AA-301 - A Phase 3, Randomized, Double-Blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Patients With Metastatic Castration-Resistant Prostate Cancer Who Have Failed Docetaxel-Based Chemotherapy</a></li><li><a href="/node/1115">NCT00887198 - COU-AA-302 - A Phase 3, Randomized, Double-blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Asymptomatic or Mildly Symptomatic Patients With Metastatic Castration-Resistant Prostate Cancer</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017

2017-1276

Project Title: 
Defining a therapeutic drug window in patients treated with infliximab for fistulizing Crohn’s disease.
Specific Aims of the Project: 

Specific Aim 1:
To investigate the association between serum infliximab concentration at week 2, 6 and 14 with fistula response, defined as a reduction of at least 50 percent from base line in the number of draining fistulas and complete fistula response, defined as the absence of draining fistulas, at week 14.

Specific Aim 2:
To investigate the association between serum infliximab trough concentration at week 14, 22, 30, 38, 46 and 54 with fistula response and complete fistula response at week 54.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Ongoing
Scientific Abstract: 

Background: Infliximab is effective treatment for fistulizing Crohn’s disease (CD). Recent exposure-response relationship studies have revealed a positive correlation between high serum infliximab concentration and favorable therapeutic outcomes, although there are only limited data regarding fistulizing CD.
Objective: To define the therapeutic window for adequate serum infliximab concentration associated with favorable therapeutic outcomes in patients with fistulizing CD for either induction or maintenance therapy.
Study Design: Post-hoc analysis of the ACCENT II study.
Participants: Patients with CD who were assessed for a fistula response following infliximab induction (n=282) or maintenance therapy (n=139).
Main outcome measure(s): Association between infliximab concentration at week 2, 6 and 14 with fistula response or complete fistula response at week 14 and association between infliximab trough concentration at week 14, 22, 30, 38, 46 and 54 with fistula response or complete fistula response at week 54.
Statistical Analysis: Descriptive statistics will be provided with medians and interquartile range for continuous variables and frequency and percentage for categorical variables. A receiver operating characteristic analysis will be performed for infliximab concentrations to trace thresholds associated with outcomes of interest. Infliximab concentrations will be compared between groups with the Mann-Whitney U and Kruskal Wallis test, as appropriate. Univariate and multivariate analyses will be performed to identify variables associated with outcomes of interest.

Brief Project Background and Statement of Project Significance: 

Fistulas can develop in up to 50% of patients with Crohn’s disease (CD), with perianal fistulas being the most common. (1) The cornerstone of pharmacological treatment for fistulizing CD is anti-tumor necrosis factor (TNF) therapy, specifically infliximab. (2) Two pivotal randomized-controlled trials clearly showed superior healing rates after induction and maintenance infliximab treatment compared to placebo. (3,4) Recent studies have revealed an exposure-response relationship suggesting a positive correlation between high serum anti-TNF drug concentration and favorable therapeutic outcomes including clinical, biomarker, and endoscopic remission. (5-10) Nevertheless, there are limited data on the therapeutic window and role of therapeutic drug monitoring (TDM) in fistula healing. (11) Moreover, as pharmacological treatment options in patients with fistulizing CD remain limited, emphasis has to be given to rational decision-making and optimization of therapies utilizing a TDM-based therapeutic approach. This project, by defining the adequate infliximab concentration for better therapeutic outcomes in patients with fistulizing CD would be crucial in better understanding necessary drug concentrations. It could serve as an important first step towards the implementation of proactive TDM in daily clinical practice and towards a ‘treat-to-trough’ therapeutic approach. This could potentially improve care and reduce the substantial social and economic burden to the community by preventing future CD-related hospitalizations and surgeries.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

Post-hoc analysis of the ACCENT II (A Crohn’s Disease Clinical Trial Evaluating Infliximab in a New Long-Term Treatment Regimen in Patients with Fistulizing Crohn’s Disease) study, a multicenter, double-blind, randomized, placebo-controlled trial, regarding only the patients who received infliximab (active drug) during induction (n=282) or maintenance therapy (n=139). (4)

Narrative Summary: 

Two pivotal randomized-controlled trials clearly showed superior healing rates of fistulas after induction and maintenance infliximab treatment compared to placebo in patients with fistulizing Crohn’s disease (CD). Serum infliximab trough concentrations have been related to favorable objective therapeutic outcomes, such as endoscopic healing. Nevertheless, there are limited data on the therapeutic window and role of therapeutic drug monitoring in fistula healing. The aim of the study is to investigate the association between serum infliximab trough concentration and positive therapeutic outcomes in patients with fistulizing CD.

Project Timeline: 

It is estimated that it will take 3-4 months to review the appropriate data. Statistical analyses will take another 3-4 months, while manuscript preparation will take approximately another 3-4 months. Consequently, the whole project will be completed in 9-12 months.

Dissemination Plan: 

Presentation of the results to national and international medical congresses including Digestive Disease Week (DDW), Advances in IBD (AIBD), American College of Gastroenterology (ACG), European Crohn’s and Colitis Organization (ECCO) and publication of the data in a high impact medical journal such as the American Journal of Gastroenterology, Clinical Gastroenterology and Hepatology, or the Journal of Crohn’s and Colitis.

Bibliography: 

1. Gecse KB, Sebastian S, Hertogh G, et al. Results of the Fifth Scientific Workshop of the ECCO [II]: Clinical Aspects of Perianal Fistulising Crohn's Disease-the Unmet Needs. J Crohns Colitis 2016;10(7):758-65.
2. Yassin NA, Askari A, Warusavitarne J, et al. Systematic review: the combined surgical and medical treatment of fistulising perianal Crohn's disease. Aliment Pharmacol Ther 2014;40(7):741-9
3. Present DH, Rutgeerts P, Targan S, et al. Infliximab for the treatment of fistulas in patients with Crohn's disease. N Engl J Med 1999;340:1398-405.
4. Sands BE, Anderson FH, Bernstein CN, et al. Infliximab maintenance therapy for fistulizing Crohn's disease. N Engl J Med 2004;350:876-85.
5. Adedokun OJ, Sandborn WJ, Feagan BG, et al. Association between serum concentration of infliximab and efficacy in adult patients with ulcerative colitis. Gastroenterology 2014;147:1296-1307.
6. Ungar B, Levy I, Yavne Y, et al. Optimizing anti-TNFα therapy: Serum levels of infliximab and adalimumab associate with mucosal healing in patients with inflammatory bowel diseases. Clin Gastroenterol Hepatol 2016;14:550-557.e2.
7. Roblin X, Marotte H, Rinaudo M, et al. Association between pharmacokinetics of adalimumab and mucosal healing in patients with inflammatory bowel diseases. Clin Gastroenterol Hepatol 2014;12:80-4.
8. Papamichael K, Van Stappen T, Vande Casteele N, et al. Infliximab concentration thresholds during induction therapy are associated with short-term mucosal healing in patients with ulcerative colitis. Clin Gastroenterol Hepatol 2016;14:543-9.
9. Reinisch W, Colombel JF, Sandborn WJ, et al. Factors associated with short- and long-term outcomes of therapy for Crohn's disease. Clin Gastroenterol Hepatol 2015;13:539-547.
10. Cornillie F, Hanauer SB, Diamond RH, et al. Postinduction serum infliximab trough level and decrease of C-reactive protein level are associated with durable sustained response to infliximab: a retrospective analysis of the ACCENT I trial. Gut 2014;63:1721-7.
11. Davidov Y, Ungar B, Bar-Yoseph H, et al. Association of induction infliximab levels with clinical response in perianal Crohn's disease. J Crohns Colitis. 2016 Oct 4. pii: jjw182. [Epub ahead of print].

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

• Main outcome measures of interest include:
1. Fistula response, defined as a reduction of at least 50% from base line in the number of draining fistulas at week 14 and 54.
2. Complete fistula response, defined as the absence of draining fistulas, at week 14 and 54.
• Secondary outcome measures of interest include:
1. C-reactive protein (CRP) normalization at week 14 and 54 in patients with an elevated CRP (>5 mg/L) at week 0.
2. Clinical remission, defined as a CD Activity Index (CDAI) score of ≤150, at week 14 and 54.
3. Clinical response, defined as a reduction from a baseline CDAI of 220 or higher by at least 25% and 70 points, at week 14 and 54.
4. Duration of rectovaginal fistula closure.
5. Composite remission, defined as both CRP normalization and complete fistula response, at week 14 and 54.
6. Loss of response throughout week 54.
7. Fistula response to treatment with increased infliximab dose at week 54.
8. Acute and/or delayed hypersensitivity infusion reactions throughout week 54.
9. Infections requiring antimicrobial treatment throughout week 54.
10. Primary non-response defined as lack of fistula response at either week 10 or week 14.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

Main predictor/independent variables associated with outcomes of interest include:
• Serum infliximab concentration at week 2, 6 and 14 associated with main and secondary outcomes of interest.
• Serum infliximab trough concentration at week 14, 22, 30, 38, 46 and 54 associated with outcomes of interest.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

Other variables associated with outcomes of interest include:
• Gender
• Age
• Disease duration
• Previous segmental resection
• CDAI at week 0
• CRP at week 0
• Concomitant immunomodulators (thiopurines / methotrexate)
• Type (single or multiple) and location (perianal, abdominal and rectovaginal) of fistulas
• Smoking history
• Antibodies to infliximab (ATI) at week 14, 30 and 54.

Statistical Analysis Plan: 

Descriptive statistics will be provided with medians and interquartile range (IQR) for continuous variables and frequency and percentage for categorical variables. A receiver operating characteristic (ROC) analysis will be performed for infliximab concentrations to trace thresholds associated with outcomes of interest. Optimal thresholds will be chosen by using the Youden index, which maximizes the sum of the specificity (SP) and sensitivity (SN) of the ROC curve as previously described.8 SN, SP, positive predictive value, and negative predictive value will be also calculated. Infliximab concentrations at week 2, 6, 14, 22, 30, 38, 46 and 54 will be compared between groups with the Mann-Whitney U test. Serum infliximab concentrations will be categorized also into quartiles. Rates of fistula response and complete fistula response as well as other (secondary) outcome measures of interest at week 14 and 54 will be compared across infliximab serum concentration quartiles with the chi-square test (linear-by-linear association). The Kruskal-Wallis and the chi-square test will be used to compare continuous or discrete variables, respectively, across quartile groups. The Mann-Whitney U test and the chi-square test or the Fisher exact test will be used for univariate analysis to identify quantitative or categorical variables associated with outcomes of interest, respectively. For endpoints defined by time to an event, such as loss of response, life table methods will be employed and the log-rank test will be used for comparisons between treatment groups. To determine the independent effects of variables associated with outcomes of interest, a multiple binary logistic regression will be then performed including variables with a P value <0.05 from univariate analysis, based on the Backward Wald selection method. The results will be expressed as odds ratio (OR) with 95% confidence intervals, followed by the corresponding P value. Results will be considered statistically significant when P <0.05. All statistical analyses will be performed by using the R and SAS statistical software.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/354">NCT00207766 - C0168T26 - ACCENT II - A Randomized, Double-blind, Placebo-controlled Trial of Anti-TNF Chimeric Monoclonal Antibody (Infliximab, Remicade) in the Long Term Treatment of Patients With Fistulizing CROHN'S Disease</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017

2016-1171

Project Title: 
Predictors of Survival in a Trial of Abiraterone Acetate for Men with Metastatic Castration-Resistant Prostate Cancer and No Prior Chemotherapy
Specific Aims of the Project: 

Aim #1: Assess the impact of additional variables on OS.
Objective #1: To incorporate additional demographic, socioeconomic and clinicopathologic variables into multivariable Cox proportional hazards models in order to identify predictors of OS.
Hypothesis #1: Non-white, unmarried and men with poor socioeconomic status will be at a survival disadvantage.

Aim #2: Assess predictors of early mortality (randomization to ≤1 year).
Objective #2: To identify predictors of early mortality (≤1 year) compared to patients that died >1 year after randomization or were alive at censoring.
Hypothesis #2: Patients with significant comorbidities and/or large metastatic burden of disease and/or rapid disease progression will be at risk for early (≤1 year) mortality.

Aim #3: (a) Assess the impact of subsequent therapy after abiraterone (or placebo for the control arm) on OS, and (b) sequence of subsequent therapy on OS.
Objective #3: To identify post-abiraterone therap(ies) that improve OS and attempt to delineate appropriate secondary therapy sequence for improving OS.
Hypothesis #3: Men receiving chemotherapy post-abiraterone will confer a survival advantage compared to those receiving other secondary therapies.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Incomplete
Scientific Abstract: 

Background: Abiraterone acetate is an androgen biosynthesis inhibitor treatment option for men with metastatic castration-resistant prostate cancer (mCRPC). A previous phase III, randomized, controlled, double-blind study compared prednisone + abiraterone or prednisone + placebo, demonstrating an overall survival (OS) benefit for those receiving abiraterone.
Objective: To identify post-hoc predictors of OS, including subsequent treatment modalities, in men previously randomized to prednisone + abiraterone or prednisone + placebo.
Study Design: Post-hoc analysis of data from a phase III, randomized, controlled, double blind study.
Participants: 1088 men with mCRPC randomized to receive prednisone + abiraterone or prednisone + placebo.
Main Outcome(s): (i) Identify demographic, socioeconomic and clinicopathologic predictors of OS; (ii) Identify predictors of early mortality (≤1 year from randomization) among men with mCRPC; (iii) Identify the most impactful combination of abiraterone (or placebo for the control arm) and subsequent treatment on OS.
Statistical Analysis: Descriptive statistics will be used to compare predictor variables between groups. Multivariable Cox Proportional hazards modelling will be used to generate hazards ratios for predictors of overall survival. The Kaplan-Meier method using the log-rank test will be used to assess median OS, stratified by groups where appropriate. Multivariable logistic regression modelling will be used to generate odds ratios for identifying predictors of early mortality (≤1 year from randomization).

Brief Project Background and Statement of Project Significance: 

Prostate cancer (PCa) is the most commonly diagnosed solid organ malignancy in the United States and remains the second leading cause of cancer deaths among men [1]. Most advanced PCa responds initially to androgen deprivation therapy (ADT), although patients ultimately progress despite castration on average between 1-3 years after starting ADT. Prior to 2004, once patients progressed on ADT, treatment was ultimately palliative. Since then, seminal articles demonstrated that docetaxel chemotherapy improves survival in men with metastatic castration-resistant prostate cancer (mCRPC) [2,3]. Subsequently, other agents (abiraterone, enzalutamide, sipuleucel-T and cabazitaxel) have been approved by the FDA in the mCRPC setting.
Abiraterone acetate is an androgen biosynthesis inhibitor that demonstrated increased survival in men with mCRPC who previously failed chemotherapy [4]. In this landmark trial, 1195 patients were randomly assigned (2:1) to receive prednisone + abiraterone vs prednisone + placebo. After 12.8 months of follow-up, patients receiving abiraterone had a significantly longer overall survival (OS) compared to the placebo group (14.8 vs 10.9 months; HR 0.65, 95%CI 0.54-0.77). A subsequent trial two years later randomizing 1088 chemo-naïve patients to either prednisone + abiraterone or prednisone + placebo also demonstrated an OS survival for patients receiving abiraterone (median not reached vs 27.2 months for prednisone alone; HR 0.75, 95%CI 0.61-0.93) [5]. A final analysis of abiraterone in chemo-naïve patients was recently published [6]. At a median follow-up of 49.2 months, 773 of 1088 men in the study had died, with a significantly improved OS in men receiving abiraterone compared to placebo (34.7 vs 30.3 months; HR 0.81, 95%CI 0.70-0.93).
In their final analysis, Ryan et al. [6] demonstrate a statistically and clinically significant improvement in OS for abiraterone at 4 years follow-up. As part of the supplementary tables of this manuscript, Ryan et al provide an exploratory multivariable analysis (MV) of OS. In this model, significant predictors of OS include abiraterone, age, and baseline PSA, LDH, alkaline phosphatase, hemoglobin, and bone metastasis. There are a number of important factors that may contribute to OS that were not assessed in this exploratory MV analysis [6] that we will assess in the proposed study: (i) the impact of demographics and socioeconomic factors (ie. Race, marital status, geographical location, etc); (ii) predictors of early (≤1 year) mortality; (iii) the impact of secondary therapies on OS, specifically the sequence of secondary therapies. In the experimental arm (abiraterone), 67% of patients had subsequent therapy and 80% of patients in the control arm (placebo) received secondary therapy. Very little is known regarding the appropriate sequence of therapy in the armamentarium of treating mCRPC and the impact on OS. This dataset provides an excellent opportunity to gain knowledge regarding additional factors that may contribute to OS and provide further granularity to the impact of subsequent therapies on OS in patients with mCRCP.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

Data source: NCT00887198 “A Phase 3, Randomized, Double-blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Asymptomatic or Mildly Symptomatic Patients with Metastatic Castration-Resistant Prostate Cancer”

Inclusion criteria (men in the above dataset):
• >18 years of age
• Complete survival data including time from randomization to death/censoring
• Complete secondary therapy data

Exclusion criteria:
Patients lost to follow-up

Narrative Summary: 

In a previous phase 3 trial of chemo-naive men with mCRCP randomized to abiraterone acetate + prednisone vs prednisone, men in the abiraterone group had a distinct survival advantage. At a median follow-up of 49.2 months, there were 741 mortalities (68% of sample). The objective of this study is to perform a post hoc analysis to determine predictors of survival. Specific predictor variables will include demographic, clinicopathologic and secondary/additional treatment. Identifying 'very high risk' men for death in an already 'at-risk' population will allow delineation of patients that should be considered for additional clinical trials, further treatment, and/or hospice care, etc.

Project Timeline: 

Anticipated project start date: within 1 month of receiving access to data (ie. February 1, 2017)
Analysis completion date: April 1, 2017
Date manuscript drafted: June 1, 2017
Manuscript first submitted for publication: August 1, 2017
Date results reported back to YODA Project: August 1, 2017

Dissemination Plan: 

This study is projected to generate an abstract for conference presentation and a high-level peer reviewed manuscript.
Anticipated conferences for presentation:
• American Society of Clinical Oncology
• European Association of Urology
• American Urological Association
• Canadian Urological Association
• Society of Urologic Oncology

Peer-Reviewed Journal Submission Plan:
• Journal of Clinical Oncology
• If rejected, then European Urology
• If rejected, then Cancer
• If rejected, then Journal of Urology
• If rejected, then Urologic Oncology
• If rejected, then BJU International
• If rejected, then Prostate Cancer and Prostatic Diseases
• If rejected, then Urology
• If rejected, then Clinical Genitourinary Cancer

Bibliography: 

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin 2016;66(1):7-30.
2. Tannock IF, de Wit R, Berry WR, et al. Docetaxel plus prednisone or mitoxantrone plus prednisone for advanced prostate cancer. N Engl J Med 2004;351(15):1502-1512.
3. Petrylak DP, Tangen CM, Hussain MH, et al. Docetaxel and estramustine compared with mitoxantrone and prednisone for advanced refractory prostate cancer. N Engl J Med 2004;351(15):1513-1520.
4. de Bono JS, Logothetis CL, Molina A, et al. Abiraterone and increased survival in metastatic prostate cancer. N Engl J Med 2011;364(21):1995-2005.
5. Ryan CJ, Smith MR, de Bono JS, et al. Abiraterone in metastatic prostate cancer without previoius chemotherapy. N Engl J Med 2013;368(2):138-148.
6. Ryan CJ, Smith MR, Fizazi K, et al. Abiraterone acetate plus prednisone versus placebo plus prednisone in chemotherapy-naïve men with metastatic castration-resistant prostate cancer (COU-AA-302): final overall survival analysis of a randomized, double-blind, placebo-controlled phase 3 study. Lancet Oncol 2015;16(2):152-160.
7. Charlson ME, Pompei P, Ales KL, MacKensize CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373-383.

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The main outcome variable in this study will be overall survival, which will be categorized as a binary variable yes/no. Time from randomization to death in months will also be recorded.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

The main predictor variable in this study will be modality of secondary/subsequent therapy. This will be defined as: none, abiraterone acetate, cabazitaxel, docetaxel, enzalutamide, ketoconazole, radium-223, spiuleucel-T.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

Other variables of interest in the study:
• Age (year, continuous)
• Race (white vs black vs other)
• Marital status (married vs unmarried vs unknown)
• Gleason score at initial diagnosis (≤7 vs ≥8 vs unknown)
• PSA at initial diagnosis (ng/mL, continuous)
• Baseline PSA at randomization (ng/mL, continuous)
• PSA doubling time (≤10 vs >10 months)
• Primary treatment (surgery vs radiation vs other vs hormonal vs none)
• Extent of disease (bone only vs soft tissue or node vs both)
• Eastern Cooperative Oncology Group (ECOG) performance status (0 vs 1)
• Charlson Comorbidity Index (CCI) (0-2 vs >2) [7]
• Brief Pain Inventory-Short Form score (0-1 vs 2-3 vs ≥4)
• Region (North America vs Other)
• LDH (U/L continuous)
• ALK-P (IU/L, continuous)
• Baseline hemoglobin (dg/L, continuous)
• Time to opiate use (months, continuous)
• Time to deterioration in ECOG performance status ≥1 point (months, continuous)
• Time to PSA progression (months, continuous)
• Time to pain progression (months, continuous)
• RECIST response (complete vs partial vs stable vs progressive)

Statistical Analysis Plan: 

Aim #1: Assess the impact of additional variables on OS.
Statistical Plan #1: Use descriptive statistics to compare patient variables (see Main Predictor & Other Variables of Interest) of those that were alive at the study conclusion to those that died. Subsequently, significant predictor variables and clinically relevant variables will then be incorporated into a multivariable Cox Proportional hazards model in order to identify predictors of OS. Median OS will then be determined using the Kaplan-Meier method using the log-rank test.

Aim #2: Assess predictors of early mortality (randomization to ≤1 year).
Statistical Plan #2: Use descriptive statistics to compare patient variables (see Main Predictor & Other Variables of Interest) of those that died ≤1 year after randomization to patients that died >1 year after randomization or were alive at censoring. Subsequently, significant predictor variables and clinically relevant variables will then be incorporated into a multivariable logistic regression model in order to identify adjusted predictors (odds ratio) of mortality ≤1 year (compared to >1 year/alive at censoring).

Aim #3: Assess the impact of subsequent therapy after abiraterone (or placebo for the control arm) on OS, and sequence of subsequent therapy on OS.
Statistical Plan #3: Use descriptive statistics to compare patient variables (see Other Variables of Interest) among groups of patients that received secondary therapy after abiraterone or placebo (see Main Predictor variable for combinations of abiraterone + subsequent treatments). Subsequently, compare treatment combinations in a multivariable Cox Proportional hazards model (adjusting for significant predictor variables and clinically relevant variables, as well as using time-varying covariates to adjust for death prior to subsequent treatment and to account for immortal time bias) in order to identify the most impactful and significant treatment combinations on OS. Median OS, stratified by treatment combination, will then be determined using the Kaplan Meier method using the log-rank test.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/1115">NCT00887198 - COU-AA-302 - A Phase 3, Randomized, Double-blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Asymptomatic or Mildly Symptomatic Patients With Metastatic Castration-Resistant Prostate Cancer</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017
Associated Data: 
Reports

2016-1107

Project Title: 
The impact of anti-TNF drug levels on rates of fistula healing in individuals with Crohn’s Disease
Specific Aims of the Project: 

Specific Aim 1: To assess the correlation of high infliximab serum trough levels with a decrease in fistula drainage greater than 50%.

Hypothesis 1: Elevated infliximab levels above are associated with improved fistula response, evidenced by a decrease in drainage by >50%

Specific Aim 2: To assess the impact of factors associated with reduced drug levels on subsequent fistula healing, employing propensity score analysis.

Hypothesis 2: Clinical factors that are associated with lower infliximab serum drug levels are also predictive of lower rates of fistula healing in Crohn’s disease

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Incomplete
Scientific Abstract: 

Background: The incidence of Crohn’s disease (CD) is increasing. A common complication of CD is fistulizing disease, affecting up to 40% of individuals. While anti-Tumor necrosis factor- (anti-TNF) drugs have demonstrated efficacy in both non-fistulizing and fistulizing disease, limited population sizes in individual clinical trials have inhibited analyses to assess what factors may predict fistula response.
Objective: To assess the impact of anti-TNF drug levels on fistula healing in CD.
Study design: Retrospective cohort study of individuals with fistulizing CD from clinical trials of anti-TNF agents.
Participants: Individuals with CD with known fistula enrolled in clinical trials of anti-TNF agents who received therapy and had subsequent drug level testing.
Main Outcome Measure(s): The primary dependent variable will be a >50% reduction in drainage from fistula measured by week 14 of the study. Additional outcomes will include > 50% reduction at 1 year and complete cessation and closure of fistula.
Statistical analysis: Baseline covariates will be compared using descriptive statistics. We will then use logistic regression to adjust for multiple covariates, assessing the impact of drug levels >5ug/mL versus <5ug/ml. We will first employ univariate analysis, including all individual covariates with a p-value >0.10 in a final combined model. Backwards elimination will then be utilized to remove variables that do not significantly impact the OR by >10%. We will also conduct a propensity score analysis adjusting for factors predictive of a drug level <5ug/ml.

Brief Project Background and Statement of Project Significance: 

The incidence of Crohn’s disease (CD) is increasing worldwide(1). Unlike the other forms of inflammatory bowel disease, the inflammation in CD is transmural, significantly increasing the risk of penetrating and stenosing phenomena such as stricture, abscesses and fistua. Enteric fistula are a particularly troublesome and common complication, affecting up to 43% of patients with CD, and are responsible for a significant amount of morbidity and patient distress. Previous therapies such as antibiotics and immunomodulators to eliminate these tracts have demonstrated marginal efficacy in case series and small randomized controlled trials(2,3).
Infliximab, a chimeric IgG1 antibody against tumor necrosis factor-α (TNF-α) has demonstrated efficacy in both inducing and maintaining remission in CD in several large randomized controlled trials(4,5). In addition, several subgroup analyses and additional studies have demonstrated the efficacy of infliximab in the treatment of fistulizing and perianal CD(6-9).
One such trial was the ACCENT-II trial, which assessed the efficacy of infliximab in 306 adult patients with at least one draining abdominal or perianal fistula6. The primary outcome assessed was the time to loss of response in those who initially had a response to infliximab, with a total of 54 weeks of follow-up time. Infliximab demonstrated a significantly increased time to loss of response (40 weeks vs 14 weeks, p<0.001) and a significantly greater number of individuals still with response at 54 weeks (46% vs 23%, p=0..001).
While these data demonstrated that infliximab had significant efficacy in the management of fistulizing disease, a proportion of patients had a loss of response (LOR), which was defined not only as recrudescence of fistula activity, but also any worsening of luminal CD or need for changes in CD therapy. On multivariate analyses, no variables were associated with increased rate of response or relapse, however.
Due to unavailability of routine drug level testing at the time, IFX levels were not assessed in the ACCENT-II trial. However, there is growing evidence that these levels are important indicators of response in those with luminal CD. We hypothesize that serum trough infliximab levels, as well as infliximab antibody levels are key variables in predicting who will respond and subsequently lose response to infliximab with fistulizing CD.
Therefore, we aim to specifically address the impact of infliximab levels on fistulizing Crohn’s disease and rates of closure and healing. To assess these aims, we will employ pooled data previously collected in the scope of clinical trials involving infliximab to assess this impact and currently available via the Yale Open Data Access Project. This will provide the largest collection of patients with fistulizing Crohn’s disease receiving infliximab studied to date. The results of this study will provide the most conclusive information available regarding the impact of anti-TNF levels on the impact of fistula healing, greatly impacting patient care.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

To achieve our stated aims of assessing the impact of infliximab serum drug levels on initial response of fistula and on subsequent maintenance of fistula response and remission, we will perform a retrospective cohort study using data previously from multiple clinical trials involving infliximab in Crohn’s as previously selected. The study population will consist of individuals enrolled in the above clinical trials with a history of Crohn’s disease, known to be complicated by 1 or more draining perianal or enterocutaneous fistula for more than 3 months, as well as women with rectovaginal fistula accompanied by at least one Enterovaginal fistula. As was allowed in the above studies, we will allow for seton use, concomittant medication use such as steroids, immunomodulators, 5-ASAs, and antibiotics. We will exclude those with other penetrating and fibrosing complications such as abscess, stricture or surgery when such data is available.

Narrative Summary: 

Crohn's disease (CD) is a chronic inflammatory condition involving the small bowel and colon. A significant and common complication of this disorder is the formation of fistulas, or abnormal connections from the bowel to other bowel segments, skin, or other organs. Fistula affect up to 40% of individuals with CD. While anti-Tumor necrosis factor-alpha (anti-TNF) drugs have demonstrated efficacy in both non-fistulizing and fistulizing disease, limited population sizes in individual clinical trials have inhibited analyses to assess what factors may predict fistula healing, particularly regarding drug levels. We aim to assess the impact of anti-TNF drug levels on fistula healing in CD.

Project Timeline: 

Upon receipt of the data, we anticipate starting the project immediately. During the first month, we would assess the quality of the received data and continuity between variable definitions where necessary. We will then begin analyzing the data, with plan to complete the analysis within the next 1-2 months. We will then draft the manuscript and abstract for this work, and anticipate 2 months for this step. Once completed, we will report our results to YODA and submit our work. We estimate that from receipt of data, the project will take an estimated 6-7 months to manuscript submission.
Based on this outline, assuming a data receipt date of 12/1/2016, we would complete data quality analysis by 2/1/2017, complete analysis by 4/1/2016, and complete the manuscript by 6/1/2016.

Dissemination Plan: 

We plan on submitting this research in both abstract form and manuscript form. We would plan to present this data at a national meeting such as Digestive Diseases Week or the annual American College of Gastroenterology annual scientific meeting. We will submit our manuscript to Gastroenterology, the preeminent journal in the field of gastroenterology.

Bibliography: 

1. Molodecky NA, Soon IS, Rabi DM, et al. Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review. Gastroenterology. 2012;142(1):46-54 e42; quiz e30.
2. Nielsen OH, Rogler G, Hahnloser D, Thomsen OO. Diagnosis and management of fistulizing Crohn's disease. Nature clinical practice. Gastroenterology & hepatology. 2009;6(2):92-106.
3. Lichtenstein GR. Treatment of fistulizing Crohn's disease. Gastroenterology. 2000;119(4):1132-1147.
4. Targan SR, Hanauer SB, van Deventer SJ, et al. A short-term study of chimeric monoclonal antibody cA2 to tumor necrosis factor alpha for Crohn's disease. Crohn's Disease cA2 Study Group. The New England journal of medicine. 1997;337(15):1029-1035.
5. Hanauer SB, Feagan BG, Lichtenstein GR, et al. Maintenance infliximab for Crohn's disease: the ACCENT I randomised trial. Lancet. 2002;359(9317):1541-1549.
6. Present DH, Rutgeerts P, Targan S, et al. Infliximab for the treatment of fistulas in patients with Crohn's disease. The New England journal of medicine. 1999;340(18):1398-1405.
7. Regueiro M, Mardini H. Treatment of perianal fistulizing Crohn's disease with infliximab alone or as an adjunct to exam under anesthesia with seton placement. Inflammatory bowel diseases. 2003;9(2):98-103.
8. Sands BE, Anderson FH, Bernstein CN, et al. Infliximab maintenance therapy for fistulizing Crohn's disease. The New England journal of medicine. 2004;350(9):876-885.
9. Sands BE, Blank MA, Patel K, van Deventer SJ. Long-term treatment of rectovaginal fistulas in Crohn's disease: response to infliximab in the ACCENT II Study. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. 2004;2(10):912-920.

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Research that confirms or validates previously conducted research on treatment effectiveness
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The primary dependent variable will be a >50% reduction in drainage from fistula measured by week 14 of the study. This will be structured as a binary variable. Secondary outcomes will include > 50% reduction at 1 year and complete cessation and closure of fistula. These will also be structured as binary variables in secondary analyses. As these are standardized outcomes used in clinical trials, we are confident that they have been recorded within the context of the original clinical trials.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

The primary independent variable will be trough infliximab level. We will perform an initial analysis dichotomizing the exposure as being present or not present on assay (i.e. drug level is detectable versus non-detectable). We will then perform a secondary analysis structuring the exposure as an ordinal variable, where 0=undetectable, 1=<5ug/mL, and 2=>5ug/ML to assess for dose-response employing ordinal logistic regression. This cut-off was selected as it is commonly employed in clinical practice.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

We will measure several covariates of interest related to CD severity and the likelihood of response, allowing us to adjust for them in our analysis. We will assess the number of fistula as a categorical variable (1 or >1), level of CD disease activity in the bowel as per Crohn’s Disease Activity Index (CDAI), whether there was clinical response to infliximab (defined as CDAI reduction of at least 70 points or >25%), duration of disease (continuous variable), steroid use (categorical), immunomodulator use (categorical), antibiotic use (categorical), seton use (categorical), tobacco use (categorical), presence of antibodies to IFX(categorical), concentration of antibodies (continuous), and need for IFX dose escalation (categorical). We will also assess several demographic factors, such as age (categorical), sex (binary), and race (categorical).

Statistical Analysis Plan: 

We will assess baseline covariates among those with IFX levels >5ug/ML compared to those with <5ug/mL, using Fisher’s exact test and chi2 test where appropriate. We will then perform univariate analysis using logistic regression assessing the relationship between our covariates and the primary outcomes. We will then construct a multivariate model including those baseline characteristics with a p-value >0.10 in the univariate analysis, along with our exposure of interest and specific variables of clinical interest. These variables, which will be forced into the model will include number of fistula, disease activity, clinical response, use of immunomodulators, and dose escalation. We will then perform backwards elimination, removing non-significant variables that are not among the group of clinically significant variables or do not modify the OR of the primary exposure of interest >10%. We will assess for interaction between immunomodulator use and the primary exposure, as there is known interaction between these variables. We will also assess for interaction between dose escalation and IFX levels.
In a secondary analysis, we will conduct a propensity score analysis looking at factors that may directly impact the drug level (treatment), and adjust for these in comparison to likelihood of fistula healing. We will also conduct sensitivity analysis examining our cut-off for drug levels and the relationship with fistula healing. We will also perform an exploratory analysis looking at the impact of the concentration of antibodies to infliximab.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/158">NCT00207675 - C0168T47 - A Randomized, Multicenter, Open-label Study to Evaluate the Safety and Efficacy of Anti-TNF a Chimeric Monoclonal Antibody (Infliximab, REMICADE) in Pediatric Subjects With Moderate to Severe CROHN'S Disease</a></li><li><a href="/node/159">NCT00094458 - C0168T67 - Multicenter, Randomized, Double-Blind, Active Controlled Trial Comparing REMICADE® (infliximab) and REMICADE plus Azathioprine to Azathioprine in the Treatment of Patients with Crohn’s Disease Naive to both Immunomodulators and Biologic Therapy (Study of Biologic and Immunomodulator Naive Patients in Crohn’s Disease)</a></li><li><a href="/node/353">NCT00207662 - C0168T21 - ACCENT I - A Randomized, Double-blind, Placebo-controlled Trial of Anti-TNFa Chimeric Monoclonal Antibody (Infliximab, Remicade) in the Long-term Treatment of Patients With Moderately to Severely Active Crohn's Disease</a></li><li><a href="/node/354">NCT00207766 - C0168T26 - ACCENT II - A Randomized, Double-blind, Placebo-controlled Trial of Anti-TNF Chimeric Monoclonal Antibody (Infliximab, Remicade) in the Long Term Treatment of Patients With Fistulizing CROHN'S Disease</a></li><li><a href="/node/355">NCT00004941 - C0168T20 - A Placebo-controlled, Repeated-dose Study of Anti-TNF Chimeric Monoclonal Antibody (cA2) in the Treatment of Patients with Enterocutaneous Fistulae as a Complication of Crohn’s Disease</a></li><li><a href="/node/985">NCT00269854 - C0168T16 - A Placebo-Controlled, Dose-Ranging Study Followed by a Placebo-Controlled, Repeated-Dose Extension of Anti-TNF Chimeric Monoclonal Antibody (cA2) in the Treatment of Patients With Active Crohn's Disease</a></li><li><a href="/node/986">C0168T16 - Efficacy and safety of retreatment with anti-tumor necrosis factor antibody (infliximab) to maintain remission in Crohn's disease.</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017

2016-1102

Project Title: 
Determine the growth and regression rate constant and the fractional cell kill of abiraterone acetate in prostate cancer
Specific Aims of the Project: 

AIM 1: Harvest data and estimate the growth and regression rates and the fractional cell kill of prostate cancer treated with abiraterone acetate.
Aim 1.1 Harvest data from patients enrolled in the study “Abiraterone acetatae + prednisone in patients with metastatic cancer castration- resistant prostate cancer who have failed docetaxel based chemotherapy” and estimate the growth and regression rates as well as the fractional cell kill while receiving therapy.
AIM 2: Assess the efficacy of abiraterone acetate as a prostate cancer therapy by establishing correlations between the rate of growth and the overall survival.
Aim 2.1: Conduct statistical comparisons of the abiraterone acetate data to data previously evaluated by the investigators from (1) publicly available databases and (2) clinical trials.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Incomplete Not Reviewed
Scientific Abstract: 

When an oncologist treating a patient with a solid tumor assesses the results at any point in time he/she is observing the occurrence of two simultaneous phenomena: regression of the fraction of tumor sensitive to the therapy and the simultaneous growth of the fraction of tumor resistant to therapy. Importantly as our extensive analysis of human data has shown, growth occurs at a rate that is constant. At any point the amount of tumor may be larger or smaller than at the outset of treatment, depending on which of these two simultaneous phenomena predominates. The rate of growth of the resistant fraction and the rate of regression of the sensitive fraction can be mathematically estimated and we have developed and extensively tested as simple, yet novel, method of assessment that can estimate these rates. As one might intuit, a faster rate of growth is worse and indeed we have shown in diverse tumors treated with a myriad of therapies that the rate of growth correlates with overall survival, the FDA gold standard. An attribute of the methodology we will use is the ability to assess efficacy independent of assessment interval. By including time of assessment in the
formulas we use to estimate the growth and regression rates, the assessment interval is inconsequential. We
propose to examine outcomes in patients undergoing treatment for prostate cancer, a common cancer with
many treatment options and with a serum marker (PSA) that is an accurate measure of disease burden.

Brief Project Background and Statement of Project Significance: 

We developed an R package, designated tumgr,9 that allowed us to obtain tumor growth rates. In cases where all parameters were significant predictors of tumor quantity (quantity at time t / quantity at time 0, given the specified cut-off value of 0.10) in more than one model, the model that minimizes the Akaike Information Criterion (AIC) will be selected. The selected model will minimize AIC with AIC = [(-2  log likelihood of model) + (2  # parameters in model)]. Patient datasets with insufficient, numerically erroneous, or missing data will not be analyzed and will be noted as excluded with one of the following explanations: no PSA data, only 1 PSA evaluation, or error data. The criteria applied to identify numerically erroneous data was patient data where both the first and last evaluations had tumor quantity values of 0 (i.e., patient enrolled and left the trial with no tumor quantity), or where a patient had only 1 unique tumor quantity value and that value was repeated for 3 or more evaluations (i.e., patient enrolled, had multiple assessment intervals, and left the trial with identical tumor quantity).

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

We developed an R package, designated tumgr, 9 that allowed us to obtain tumor growth rates. In cases where all parameters were significant predictors of tumor quantity (quantity at time t /quantity at time 0, given the specified cut-off value of 0.10) in more than one model, the model that minimizes the Akaike Information Criterion (AIC) will be selected. The selected model will minimize AIC with AIC = [(-2  log likelihood of model) + (2  # parameters in model)]. Patient datasets with insufficient, numerically erroneous, or missing data will not be analyzed and will be noted as excluded with one of the following explanations: no PSA data, only 1 PSA evaluation, or error data. The criteria applied to identify numerically erroneous data was patient data where both the first and last evaluations had tumor quantity values of 0 (i.e., patient enrolled and left the trial with no tumor quantity), or where a patient had only 1 unique tumor quantity value and that value was repeated for 3 or more evaluations (i.e., patient enrolled, had multiple assessment intervals, and left the trial with identical tumor quantity).

Narrative Summary: 

Treatment of prostate cancer with chemotherapy drugs leads to regression of the fraction of tumor sensitive to the therapy and simultaneous growth of the fraction of tumor resistant to the therapy. In previous work with a diverse group of cancers including prostate caner we have shown that the rate of both of these process occur simultaneously. Using a novel method of analysis we can discern these two simultaneous processes and establish for each tumor its rate of growth and regression while treatment is administered. Importantly in many cancers but most importantly in prostate cancer, the rate of growth while receiving treatment correlates highly with overall survival, the FDA gold standard

Project Timeline: 

0-3 months: Analyze data
6 months: Assess efficacy and compare results to other therapies
12 months : Submit for publication

Dissemination Plan: 

Publish in top tier journal

Bibliography: 

1. Stein WD, Figg WD, Dahut W, et al. Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data. Oncologist 2008; 13:1046–1054.
2. Stein WD, Yang J, Bates SE, Fojo T. Bevacizumab reduces the growth rate constants of renal carcinomas: a novel algorithm suggests early discontinuation of bevacizumab resulted in a lack of survival advantage. Oncologist 2008; 13:1055–1062.
3. Stein WD, Huang H, Menefee M et al. Other paradigms: growth rate constants and tumor burden determined using computed tomography data correlate strongly with the overall survival of patients with renal cell carcinoma. Cancer J. 2009; 15:441-7.
4. Stein WD, Gulley JL, Schlom J et al. Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: the growth rate constant as an indicator of therapeutic efficacy. Clin Cancer Res 2011; 17:907–917.
5. Stein WD, Wilkerson J, Kim ST, et al. Analyzing the pivotal trial that compared sunitinib and IFN-alpha in renal cell carcinoma, using a method that assesses tumor regression and growth. Clin Cancer Res 2012; 18:2374–2381.

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Rate of growth
Rate of regression
Fractional cell kill

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

Rate of growth
Rate of regression
Fractional cell kill

Statistical Analysis Plan: 

Patient datasets with sufficient data will be analyzed and noted using the novel formulae and in dictated as either included (with selected model indicated) or excluded (non-significant predictors where no model converged indicated as ‘not fit’ or those with only 2 data points differing by <20%). Comparisons of growth rate distributions will done by Wilcoxon two-sided tests (where groups analyzed = 2) or by Kruskal Wallis tests (where groups analyzed >2) followed by a Dunn’s test for pairwise difference if there is an overall difference. The Kaplan-Meier method will be used to estimate OS probabilities. Landmark survival analysis of OS will be performed using a landmark (in month) and a Cox regression will be performed with the log of g (estimated from data prior to landmark) as the single predictor using the R package survival to obtain a measure of concordance (C-index) between g and OS. Landmark number month will be chosen as a time point that will be far enough after the initiation of treatment to allow for reliable estimation of g, but close enough in time to randomization so that a limited number of patients have already died. This time point will be pre-specified and will be the only time point examined. Additionally, the incremental value of g was evaluated by comparing a Cox model containing baseline variables (age, race, treatment) with a model containing baseline variables and g, to obtain the change in the C-index after the addition of g information using 1000 iterations of perturbation re-sampling via the R package survC1. By incremental g evaluation we are looking to define how much additional model accuracy (as assessed by the C-index) the addition of g to the model added.
Finally, because the formulae used will include time (t), the analysis is not affected by assessment intervals such that if the intervals of two studies are different or if scheduling difficulties require some intervals to be longer or shorter the estimates of phi, g and d, are not affected since these estimates are a global average over all data points for that patient. This in turn allows the data to be presented as one output. Note also that estimates of phi are determined not only by the falling part of the tumor size curve (PSA as surrogate for this) but also by data form the re-growing phase. Links to the programs used can be found at the end of the manuscript.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/304">NCT00638690 - COU-AA-301 - A Phase 3, Randomized, Double-Blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Patients With Metastatic Castration-Resistant Prostate Cancer Who Have Failed Docetaxel-Based Chemotherapy</a></li></ol>
Make Publicly Available : 

2016-1088

Project Title: 
Finite sample properties of synthetic control estimators
Specific Aims of the Project: 

The specific aim of the project is to see whether researchers should you 1 lag, 2 lags, or every possible lag of the pre-treatment outcome in the synthetic control model. The way to assess this is to compare the treatment effect generated by the SCM method to the true treatment effect.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Incomplete Not Reviewed
Scientific Abstract: 

Research the effectiveness of the synthetic control statistical estimator in generating unbiased estimates of the treatment effect on the treated (TOT)

Brief Project Background and Statement of Project Significance: 

This research will examine how investigators can effectively use the synthetic control method to obtain the counterfactual outcome for the treated unit. Generating unbiased estimates of the counterfactual is extremely important to accurately assess the effectiveness of a treatment.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

The statistical method to be used is the synthetic control method, was which originally developed by Abadie. et. al. in 2003. In this method, a "synthetic" unit is created for each unit that receives the treatment, and the synthetic unit is composed as a weighted combination of units that did not receive the treatment.

Narrative Summary: 

This project aims to construct a counterfactual estimate of health outcomes for treated individuals. It also aims to compare the effect of the treatment using the SCM method to the true effect of the treatment.

Project Timeline: 

Start date: As soon as data is received
Analysis completion date and results reported to YODA: 6 months after having received the data
Manuscript drafted and first submitted for publication: 1 year.

Dissemination Plan: 

Target audience: Applied microeconomists and statisticians.
Journals: RES, AER, AEJ, etc.

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The main outcome measure is the difference between the true treatment effect, and the treatment effect generated by the synthetic control method.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

All independent variables available are fed into the synthetic control method. The synthetic control model then appropriately weights the independent variables.

Statistical Analysis Plan: 

I will analyze the clinical data by performing the following statistical techniques to generate the effectiveness of the treatment: 1) a multivariate regression analysis with a dummy variable for a unit receiving treatment 2) A difference in difference analysis, where each unit receiving the treatment is compared to a single control unit 3) The synthetic controls analysis, where I will compare the treatment effect using SCM to other estimates of the treatment effect.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/304">NCT00638690 - COU-AA-301 - A Phase 3, Randomized, Double-Blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Patients With Metastatic Castration-Resistant Prostate Cancer Who Have Failed Docetaxel-Based Chemotherapy</a></li></ol>
Make Publicly Available : 

2016-1046

Project Title: 
Serum infliximab concentration and prevention of clinical or endoscopic post-operative recurrence after an ileocolonic resection for Crohn’s disease
Specific Aims of the Project: 

Study Objective: To define the therapeutic window for adequate infliximab concentration associated with favorable therapeutic outcomes in CD patients who receive prophylactic infliximab therapy after an ileocolonic resection for prevention of clinical or endoscopic post-operative recurrence.

Specific Aim 1:
To investigate the association between serum infliximab concentration at week 72 and favorable therapeutic outcomes, including post-operative endoscopic response and remission at week 76, post-operative sustained clinical remission at week 76 and 104 and deep remission at week 76.

Specific Aim 2:
To investigate the association between serum infliximab concentration at week 72 and severe post-operative endoscopic recurrence at week 76.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Incomplete
Scientific Abstract: 

Background: Anti-tumor necrosis factor (TNF) therapy is effective for preventing endoscopic or clinical post-operative recurrence (POR) in patients with Crohn’s disease (CD) and an ileocolonic resection. Recent exposure-response relationship studies have revealed a positive correlation between high serum anti-TNF drug concentration and positive clinical outcomes.
Objective: To define the therapeutic window for adequate infliximab concentration associated with favorable therapeutic outcomes in CD patients who receive prophylactic infliximab therapy after an ileocolonic resection for prevention of clinical or endoscopic POR.
Study Design: Post-hoc analysis of the PREVENT study.
Participants: Patients (n=147) who started 5 mg/kg of infliximab every 8 weeks after randomization.
Main outcome measure(s): Association between serum infliximab concentration at week 72 and post-operative endoscopic remission at week 76, sustained clinical remission at week 76 and 104 and deep remission at week 76.
Statistical Analysis: Descriptive statistics will be provided with medians and interquartile range for continuous variables and frequency and percentage for categorical variables. A receiver operating characteristic analysis will be performed for infliximab concentrations to trace thresholds associated with outcomes of interest. Infliximab concentrations will be compared between groups with the Mann-Whitney U and Kruskal Wallis test, as appropriate. Univariate and multivariate analyses will be performed to identify variables associated with outcomes of interest.

Brief Project Background and Statement of Project Significance: 

Anti-tumor necrosis factor (TNF) therapy is effective for both prevention and treatment of clinical or endoscopic post-operative recurrence (POR) in patients with Crohn’s disease (CD) and an ileocolonic resection.[1-4] Recent studies have revealed an exposure-response relationship suggesting a positive correlation between high serum anti-TNF drug concentration and favorable therapeutic outcomes including clinical, biomarker, and endoscopic remission.[5-11] Nevertheless, there are only limited data regarding the role of therapeutic drug monitoring (TDM) of infliximab when administered prophylactically for prevention of clinical and/or endoscopic POR after an ileocolonic resection for CD.[12,13] Moreover, as treatment options in CD patients who fail anti-TNF therapy and undergo an ileocolonic resection remain currently still limited, emphasis has to be given on rational decision-making, such as the TDM-based therapeutic approach. This project, by defining the adequate infliximab concentration for preventing negative therapeutic outcomes in CD patients after an ileocolonic resection, could be an important first step towards the implementation of a proactive TDM in daily clinical practice. This could potentially improve the patients’ care and reduce the substantial social and economic burden to the community by preventing future CD-related hospitalizations and surgeries.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

Post-hoc analysis of the PREVENT (Prospective, Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial Comparing REMICADE® [infliximab] and Placebo in the Prevention of Recurrence in Crohn’s Disease Patients Undergoing Surgical Resection Who Are at an Increased Risk of Recurrence; ClinicalTrials.gov ID NCT01190839) study, a phase 3, multicenter, placebo-controlled, double-blind, randomized study conducted at 104 sites globally between November 2010 and May 2012 regarding only the patients who started 5 mg/kg of infliximab every 8 weeks after randomization (n=147). [1]

Narrative Summary: 

Crohn’s disease (CD) often requires an ileocolonic resection when pharmacological treatment fails. However, 30% of patients will require a second resection within 10 years. This can be prevented by anti-tumor necrosis factor (TNF) therapy. Evaluation of serum drug concentration and anti-drug antibodies can optimize efficacy and cost of anti-TNF therapy. The aim of the study is to investigate the association between drug concentration and positive therapeutic outcomes in CD patients receiving prophylactic infliximab therapy after an ileocolonic resection. This could improve patients’ care potentially reducing the substantial social and economic burden to the community.

Project Timeline: 

It is estimated that it will take 2-3 months to review the appropriate data. Statistical analyses will take another 2-3 months, while manuscript preparation will take approximately 2-3 months. Consequently, the whole project will be completed in 6-9 months.

Dissemination Plan: 

The results of this study will be disseminated to patients and care-givers through presentations to national and international medical congresses including DDW), Crohn’s and Colitis Foundation of America (CCFA), American College of Gastroenterology (ACG), European Crohn’s and Colitis Organization (ECCO) and publication of the data in a high impact medical journal such as the American Journal of Gastroenterology, Clinical Gastroenterology and Hepatology, or the Journal of Crohn’s and Colitis and distribution to patients’ societies.

Bibliography: 

1. Regueiro M, Feagan BG, Zou B, et al. Infliximab reduces endoscopic, but not clinical, recurrence of Crohn's Disease after ileocolonic resection. Gastroenterology 2016 Mar 3. pii: S0016-5085(16)00293-6
2. De Cruz P, Kamm MA, Hamilton AL, et al. Crohn's disease management after intestinal resection: a randomised trial. Lancet 2015;385:1406-17.
3. Regueiro M, Kip KE, Baidoo L, et al. Postoperative therapy with infliximab prevents long-term Crohn's disease recurrence. Clin Gastroenterol Hepatol 2014;12:1494-502.
4. Papamichael K, Archavlis E, Lariou C, et al. Adalimumab for the prevention and/or treatment of post-operative recurrence of Crohn's disease: a prospective, two-year, single center, pilot study. J Crohns Colitis 2012;6:924-31.
5. Papamichael K, Cheifetz AS. Use of anti-TNF drug levels to optimise patient management. Frontline Gastroenterol doi:10.1136/flgastro-2016-100685
6. Papamichael K, Cheifetz AS. Higher adalimumab drug levels are associated with mucosal healing in patients with Crohn's disease. J Crohns Colitis 2016;10:507-9.
7. Ungar B, Levy I, Yavne Y, et al. Optimizing anti-TNFα therapy: Serum levels of infliximab and adalimumab associate with mucosal healing in patients with inflammatory bowel diseases. Clin Gastroenterol Hepatol 2016;14:550-557.e2.
8. Roblin X, Marotte H, Rinaudo M, et al. Association between pharmacokinetics of adalimumab and mucosal healing in patients with inflammatory bowel diseases. Clin Gastroenterol Hepatol 2014;12:80-4.
9. Papamichael K, Van Stappen T, Vande Casteele N, et al. Infliximab concentration thresholds during induction therapy are associated with short-term mucosal healing in patients with ulcerative colitis. Clin Gastroenterol Hepatol 2016;14:543-9.
10. Reinisch W, Colombel JF, Sandborn WJ, et al. Factors associated with short- and long-term outcomes of therapy for Crohn's disease. Clin Gastroenterol Hepatol 2015;13:539-547.
11. Cornillie F, Hanauer SB, Diamond RH, et al. Postinduction serum infliximab trough level and decrease of C-reactive protein level are associated with durable sustained response to infliximab: a retrospective analysis of the ACCENT I trial. Gut 2014;63:1721-7.
12. Sorrentino D, Marino M, Dassopoulos T et al. Low Dose Infliximab for Prevention of Postoperative Recurrence of Crohn's Disease: Long Term Follow-Up and Impact of Infliximab Trough Levels and Antibodies to Infliximab. PLoS One 2015;10:e0144900.
13. Bodini G, Savarino V, Peyrin-Biroulet L, et al. Low serum trough levels are associated with post-surgical recurrence in Crohn's disease patients undergoing prophylaxis with adalimumab. Dig Liver Dis 2014;46:1043-6.
14. Rutgeerts P, Geboes K, Vantrappen G, et al. Predictability of the postoperative course of Crohn’s disease. Gastroenterology 1990;99:956–963.
15. Best WR, Becktel JM, Singleton JW, et al. Development of a Crohn’s disease activity index. National Cooperative Crohn’s Disease Study. Gastroenterology 1976;70:439–444.
16. Adedokun OJ, Sandborn WJ, Feagan BG, et al. Association between serum concentration of infliximab and efficacy in adult patients with ulcerative colitis. Gastroenterology 2014;147:1296–1307.

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

• Post-operative endoscopic response, defined as a Rutgeerts score of equal or lower than R-i1 [14], at week 76.
• Post-operative endoscopic remission, defined as a Rutgeerts score of R-i0, at week 76.
• Post-operative sustained clinical remission, defined as a CD Activity Index (CDAI) score of equal or lower than 150 [15], at week 76 and week 104.
• Deep remission, defined as both endoscopic and clinical remission, at week 76.
• Severe post-operative endoscopic recurrence, defined as a Rutgeerts score of equal or greater than R-i3, at week 76.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

• Serum infliximab concentration and antibodies to infliximab (ATI) at week 72.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

• Gender.
• Race.
• Age.
• Disease duration.
• Involved gastrointestinal areas.
• Findings at surgery.
• Age at first ileocolonic resection.
• Concomitant Immunosuppressive drugs.
• Prior Immunosuppressive drugs (thiopurines / methotrexate).
• Prior anti-TNF therapy therapy.
• Prior infliximab therapy.
• Prior intra-abdominal surgeries.
• CDAI score.

Statistical Analysis Plan: 

Descriptive statistics will be provided with medians and interquartile range (IQR) for continuous variables and frequency and percentage for categorical variables. A receiver operating characteristic (ROC) analysis will be performed for infliximab concentrations to trace thresholds associated with outcomes of interest. Optimal thresholds will be chosen by using the Youden index, which maximizes the sum of the specificity (SP) and sensitivity (SN) of the ROC curve as previously described.[16] SN, SP, positive predictive value, and negative predictive value will be also calculated. Infliximab concentrations at week 72 will be compared between groups with the Mann-Whitney U test. Serum infliximab concentrations will be categorized also into quartiles. Rates of post-operative endoscopic response or remission at week 76, post-operative sustained clinical remission at week 76 and 104, deep remission at week 76 and severe post-operative endoscopic recurrence at week 76 will be compared across infliximab serum concentration quartiles at weeks 72 with the chi-square test (linear-by-linear association). The Kruskal-Wallis and the chi-square test will be used to compare continuous or discrete variables, respectively, across quartile groups. The Mann-Whitney U test and the chi-square test or the Fisher exact test will be used for univariate analysis to identify quantitative or categorical variables associated with outcomes of interest, respectively. To determine the independent effects of variables associated with outcomes of interest, a multiple binary logistic regression will be then performed including variables with a P value <0.05 from univariate analysis, based on the Backward Wald selection method. The results will be expressed as odds ratio (OR) with 95% confidence intervals, followed by the corresponding P value. Results will be considered statistically significant when P <0.05. All statistical analyses will be performed by using the SPSS 22.0 software (SPSS, Chicago, IL) and GraphPad Prism version 5.03 for Windows (GraphPad Software, San Diego, CA).

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/984">NCT01190839 - REMICADECRD3001 - Prospective, Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial Comparing REMICADE (Infliximab) and Placebo in the Prevention of Recurrence in Crohn's Disease Patients Undergoing Surgical Resection Who Are at Increased Risk of Recurrence</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017
Associated Data: 
Reports

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