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  ["property_scientific_abstract"]=>
  string(2387) "BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disorder which has been estimated to affect 5-7% of children and 2.5% of adults worldwide. It is characterised by inattention and/or hyperactivity-impulsivity, that lead to impairment in the family, classrooms, workplaces and social settings and can prevent individuals from maintaining relationships and reaching their full potential. A number of pharmacologic and non-pharmacologic treatments are available. A network meta-analysis (NMA) of published and unpublished data provided the most comprehensive evidence on the relative efficacy, safety and tolerability of pharmacological treatments across the lifespan at the group level. 
NMA is a statistical method that uses data from multiple randomised controlled trials (RCTs) and aims to estimate the relative effects of all available treatments for a certain condition. Common applications of NMA use aggregate data and estimate average treatment effects, i.e., treatment effects at the group level. The analysis of aggregate data, although useful on its own, cannot estimate treatment effects for specific types of patients, i.e., it cannot account for important differences in characteristics of the patients that may significantly moderate treatment effects (e.g., previous exposure to medication, specific comorbidities and severity of symptoms).
OBJECTIVE: The overall objective of the IPD-NMA is the establishment of a hierarchy of the efficacy and acceptability/tolerability of the available pharmacological treatments for ADHD in sub-groups of children, adolescents or adults with particular clinical characteristics (see https://esm.ispm.unibe.ch/shinies/iCBT/ for an example). This will provide evidence to inform future clinical guidelines, support personalised approaches in the treatment of ADHD, and identify areas for further research.
DESIGN: An Individual Patient Data Network Meta-Analysis (IPD-NMA)
PARTICIPANTS: This NMA has been designed to use data from individual participants recruited in RCTs of ADHD medications, to enable more detailed and flexible analyses to be performed than those based on aggregate data.
MAIN OUTCOME MEASURES: Primary outcome is effacacy, secondary outcomes included tolerability and acceptability
STATISTICAL ANALYSIS: Two stage IPD-NMA" ["project_brief_bg"]=> string(3060) "For ADHD, as for many other disorders, clinicians, patients and parents/carers are faced with a range of possible medications to choose from. In the absence of evidence-based biomarkers and clinical predictors of response or adverse effects, currently treatment selection in the clinical practice often relies on trial-and-error [7]. This results in protracted periods of sub-optimal treatment until the drug that provides the optimum balance of efficacy and tolerability for an individual patient is identified.
The most robust empirical evidence for the effects of treatments comes from RCTs. The results of these can be statistically combined in pairwise Meta-Analyses (MAs) to estimate the relative effects of two drugs (or one drug and placebo). When a range of treatment options are available, the utility of pairwise comparisons from MAs for selection of treatment is limited. NMAs overcome this limitation by comparing multiple treatments simultaneously in a single analysis to produce estimates of relative effects among all interventions [11]. In addition to including data on treatment comparisons provided directly from RCTs, NMAs also incorporate indirect information, obtained by combining data from RCTs of different drugs evaluated against the same comparator. NMAs facilitate initial treatment selection by providing estimates of the extent to which patients with a clinical condition are likely to benefit from and/or not be harmed by a particular medication, as compared to a reference treatment (e.g., active drug or placebo).
However, NMA results based on aggregate data only estimate the average treatment effects, i.e., at the group level. Individual Patient Data Network Meta-Analyses (IPD-NMAs) analyse patient-level rather that group-level data. This type of analysis also has the capacity to provide estimates of the effects of medication on subgroups of patients with specific characteristics, such as previous exposure to medications, severity of symptoms and comorbidities which could moderate the effects of medications [12]. Additionally, an IPD-NMA has other advantages as compared to aggregate data NMAs, such as the ability to standardise analyses across different studies, harmonise the definition of outcomes, offset inadequate reporting of individual studies, and allow more accurate assessment of study quality [13] [14].
Data for IPD-NMAs may be obtained from trial investigators and sponsors, including pharmaceutical companies, either directly or via data-sharing repositories and platforms. In addition to published data, the datasets may include information on unreported outcomes, allowing additional comparisons to be made. The data collected are first re-examined and verified, and any discrepancies found are checked with authors. As well as re-analyses of data reported in trial publications, additional analyses can be performed on data not reported in trial publications, new hypotheses can be tested and data from participants excluded from the original analyses can be included [15]." ["project_specific_aims"]=> string(1357) "AIM: To collect and analyse individual patient data (IPD) from published and unpublished RCTs of FDA-approved pharmacological treatments for ADHD in children, adolescents, and/or adults.
The overall objective of the IPD-NMA is the establishment of a hierarchy of the efficacy and acceptability/tolerability of the available pharmacological treatments for ADHD in sub-groups of children, adolescents or adults with particular clinical characteristics (see https://esm.ispm.unibe.ch/shinies/iCBT/ for an example). This will involve:
i. assessing pharmacological treatments in patients with ADHD, in terms of:
? overall efficacy on severity of ADHD core symptoms;
? tolerability (defined as dropouts due to adverse events);
? acceptability (defined as dropouts due to any cause);
? changes in body weight changes in blood pressure and in heart rate
? individual adverse events
ii. exploring interactions between treatment effects and potential moderators, to identify predictors of individual response to medication.
iii. re-analysing data to reduce clinical heterogeneity and network inconsistency, by controlling for any patient-level moderators identified.
iv. using the data to conduct time-to-event analyses.
v. IPD may include additional information compared to aggregate data" ["project_study_design"]=> array(2) { ["value"]=> string(7) "meta_an" ["label"]=> string(52) "Meta-analysis (analysis of multiple trials together)" } ["project_study_design_exp"]=> string(0) "" ["project_purposes"]=> array(0) { } ["project_purposes_exp"]=> string(0) "" ["project_software_used"]=> string(0) "" ["project_software_used_exp"]=> string(0) "" ["project_research_methods"]=> string(402) "The selection of studies for the IPD-NMA follows the same criteria as in our previously published an aggregate data NMA [16], with the addition of including the newly FDA approved ADHD medication ? Viloxazine. The same study selection, inclusion and exclusion criteria are being used for this IPD NMA will be published in the study protocol.
Participants will include children (aged ?5 years and" ["project_main_outcome_measure"]=> string(1173) "Primary outcome
Observer-rated efficacy: the endpoint scores for the severity of core symptoms of ADHD (combined score of inattention, hyperactivity and impulsivity) measured on validated ADHD rating scales will be used. For adults, efficacy will be based on ratings by clinicians and, for children and adolescents, ratings by clinicians and teachers will be used.
In the first instance we aim to use the same rating scale for all studies. If studies do not report scores from the same rating scale, we will convert different scores into one single score from the same rating scale, ADHD-RS, as done by Furukawa et al [18]. In order to convert, we need studies reporting scores from different rating scales for the same outcome. If we are able to convert everything into ADHD-RS, we will synthesise data as a mean difference.
If we are not able to convert different scales into ADHD-RS, we will keep the original scales and will do the analysis using the standardized mean difference. We will use the following hierarchy:
1. ADHD-RS
2. SNAP;
3. Conner?s rating scale (any version);
4. Other ADHD scales (e.g., SKAMP, PERMP)." ["project_main_predictor_indep"]=> string(61) "Please see table 1 attached for list of variables of interest" ["project_other_variables_interest"]=> string(61) "Please see table 1 attached for list of variables of interest" ["project_stat_analysis_plan"]=> string(3757) "Our aim is to estimate the patient-specific effectiveness and safety of each drug, to facilitate a personalised choice of treatment. Our main hypothesis is that routinely collected patient-level covariates (such as disease and medication history, demographics, etc.) can be used to predict to some extent the effects of the various drugs on the outcomes of the individual patients, to guide a personalized choice of treatment. The overall target of our analysis is to estimate relative effects between all interventions with respect to [19] the endpoint ADHD symptom score, [20] the probability of dropping out of treatment and [21] the probability of specific side effects for a patient with certain characteristics after 12 weeks of treatment. To maximize the power to detect modifications of the drug effects according to patient characteristics, we opt to synthesise all data that becomes available. Consequently, our sample size will be the maximum available, subject to the responses from data owners. In cases of treatment switch during the randomised phase, patients will be analysed according to the drug they were initially allocated, i.e., following an intention-to-treat analysis.
It is very often the case that for some patients we do not have complete information on all the covariates of interest, or the outcomes. Analysing only patients with complete records will reduce our statistical power and may bias results, when outcomes are not missing completely at random. To retain patients with missing data in our analyses, we will create 10 multiply imputed datasets, while taking into account the stratification of patients in studies. This will be based on the missing at random assumption. We will use all covariates and outcomes for our imputation models. For implementation, we will use the jomo package in R [22].
Individual patient data network meta-analyses
We will employ a two stage IPD-NMA. At the first stage, we will analyse each study separately, using the 10 multiply imputed datasets [28]. We will fit a Bayesian model including linear interactions between treatment and a list of predefined suspected effect modifiers. These include: age, gender, weight, height, baseline ADHD score, comorbid psychiatric disorder and baseline quality of life score. For binary covariates we will use a binomial likelihood, or, if deemed possible according to data availability, we will do a time-to-event analysis. To avoid overfitting and aiming to have better generalizability of our model, we will use a Bayesian LASSO prior distribution for all treatment-covariate interactions [29]. Next, the posterior estimates from all multiply imputed datasets will be combined into a final posterior distribution [30]. Using this distribution, we will summarize the study-specific estimates for average treatment effect and treatment-covariate interactions as well as their variance-covariance matrix from each study. Finally, we will synthesize the study-specific point estimates and variance covariance matrices across the whole network assuming consistency, using multivariate normal distributions. As in the analysis of aggregate data, we will assume a common heterogeneity parameter across the whole network. The final output of this analysis will be estimates of relative effects and 95% Credible Intervals for all treatment comparisons, for all levels of the effect modifiers. To facilitate the uptake of our IPD NMA results, we will build an online tool (for an example see https://esm.ispm.unibe.ch/shinies/iCBT/), where the estimates of our models will be used in conjunction with input regarding baseline patient characteristics to estimate patient-level relative effects regarding all interventions in the network." ["project_timeline"]=> string(205) "12-36 months
Project start date: 01.01.2023
Data harmonisation: 6-12 months
Data analysis: 6 months
Data results reported back to YODA within 12 months of analysis completion" ["project_dissemination_plan"]=> string(375) "Publication of the results from each phase of the project in peer-reviewed, scientific journals and dissemination of the findings to the general audience via patient and public engagement. Potential journals include - Lancet Psychiatry, Lancet, JAMA.
We will ensure that the data providers are notified and have the opportunity to view the results before publication." ["project_bibliography"]=> string(5494) "

1. Faraone, S.V., et al., Attention-deficit/hyperactivity disorder. Nature Reviews Disease Primers, 2015. 1(1): p. 15020.
2. Polanczyk, G.V., et al., ADHD prevalence estimates across three decades: an updated systematic review and meta-regression analysis. International Journal of Epidemiology, 2014. 43(2): p. 434-442.
3. Simon, V., et al., Prevalence and correlates of adult attention-deficit hyperactivity disorder: meta-analysis. British Journal of Psychiatry, 2009. 194(3): p. 204-211.
4. Lara, C., et al., Childhood Predictors of Adult Attention-Deficit/Hyperactivity Disorder: Results from the World Health Organization World Mental Health Survey Initiative. Biological Psychiatry, 2009. 65(1): p. 46-54.
5. Wolraich, M.L., et al., Clinical Practice Guideline for the Diagnosis, Evaluation, and Treatment of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. Pediatrics, 2019. 144(4): p. e20192528.
6. (NICE), N.I.f.H.a.C.E., Attention deficit hyperactivity disorder: diagnosis and management, NICE guideline [NG87. 2019 Published 14 March 2018. Last update 13 September 2019.)
7. Cortese, S., Pharmacologic Treatment of Attention Deficit-Hyperactivity Disorder. N Engl J Med, 2020. 383(11): p. 1050-1056.
8. Chai, G., et al., Trends of Outpatient Prescription Drug Utilization in US Children, 2002?2010. Pediatrics, 2012. 130(1): p. 23.
9. Renoux, C., et al., Prescribing trends of attention-deficit hyperactivity disorder (ADHD) medications in UK primary care, 1995-2015. Br J Clin Pharmacol, 2016. 82(3): p. 858-68.
10. Cortese, S., et al., Comparative efficacy and tolerability of medications for attention-deficit hyperactivity disorder in children, adolescents, and adults: a systematic review and network meta-analysis. Lancet Psychiatry, 2018. 5(9): p. 727-738.
11. Cipriani, A., et al., Conceptual and Technical Challenges in Network Meta-analysis. Annals of Internal Medicine, 2013. 159(2): p. 130-137.
12. Veroniki, A.A., et al., Methods and characteristics of published network meta-analyses using individual patient data: protocol for a scoping review. BMJ Open, 2015. 5(4): p. e007103.
13. Debray, T.P.A., et al., Individual Participant Data (IPD) Meta-analyses of Diagnostic and Prognostic Modeling Studies: Guidance on Their Use. PLOS Medicine, 2015. 12(10): p. e1001886.
14. Tierney JF, S.L., Clarke M. Individual participant data., Individual participant data. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.0 (updated July 2019). Cochrane, 2019, 2019. Chapter 26.
15. Veroniki, A.A., et al., Methods and characteristics of published network meta-analyses using individual patient data: protocol for a scoping review. BMJ Open, 2015. 5(4): p. e007103.
16. Mavridis, D., et al., A primer on network meta-analysis with emphasis on mental health. Evid Based Ment Health, 2015. 18(2): p. 40-6.
17. Curtin, F., D.G. Altman, and D. Elbourne, Meta-analysis combining parallel and cross-over clinical trials. I: Continuous outcomes. Stat Med, 2002. 21(15): p. 2131-44.
18. Furukawa TA, et al. Translating the BDI and BDI-II into the HAMD and vice versa with equipercentile linking. Epidemiol Psychiatr Sci. 2019 (29)
19. Debray, T. P., Schuit, E., Efthimiou, O., et al.,. An overview of methods for network meta-analysis using individual participant data: when do benefits arise? Statistical Methods in Medical Research, 2018, 27(5), 1351?1364.
20. Efthimiou, O., Debray, T. P. A., van Valkenhoef, G., et al., GetReal in network meta?analysis: a review of the methodology. Res. Syn. Meth., 2016, 7: 236? 263.
21. Didden, E.-M., Ruffieux, Y., Hummel, N.,et al. Prediction of Real-World Drug Effectiveness Prelaunch: Case Study in Rheumatoid Arthritis. Medical Decision Making, 2018, 38(6), 719?729.
22. Quartagno M, Grund S, Carpenter J. jomo: A Flexible Package for Two-level Joint Modelling Multiple Imputation. R Journal 2019.
23. Rcker G, Schwarzer G, Krahn U. netmeta: network meta-analysis using Frequentist methods. R package version 0.9-8, 2018. 2019
24. Turner R, et al., Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis. Statistics in Medicine. 2014 (34) p984-998
25. Knig J, Krahn U, Binder H. Visualizing the flow of evidence in network meta?analysis and characterizing mixed treatment comparisons. Statistics in medicine 2013; 32(30): 5414-29.
26. 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
27. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Bmj 1997; 315(7109): 629-34
28. Debray, T. P., Schuit, E., Efthimiou, O., et al. An overview of methods for network meta-analysis using individual participant data: when do benefits arise? Statistical Methods Med Res. 2018, 27(5) 1351-1364
29. Seo M., et al., Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis. Statistics in Medicine. 2020, 40(6) 1553-1573
30. Zhou X, Reiter JP. A note on Bayesian inference after multiple imputation. The American Statistician 2010; 64(2): 159-63

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2022-4930

General Information

How did you learn about the YODA Project?: Data Holder (Company)

Conflict of Interest

Request Clinical Trials

Associated Trial(s):
  1. NCT00246220 - A Multicentre, Randomised, Double-Blind, Placebo-Controlled, Parallel Group, Dose-Response Study To Evaluate the Safety And Efficacy Of Prolonged Release OROS Methylphenidate Hydrochloride (18, 36 and 72 mg/Day), With Open-Label Extension, In Adults With Attention Deficit/Hyperactivity Disorder
  2. NCT00714688 - A Multicentre, Randomized, Double-Blind, Placebo-Controlled, Parallel Group, Dose-Response Study to Evaluate Efficacy and Safety of Prolonged Release (PR) OROS Methylphenidate (54 and 72 mg/Day) in Adults With Attention Deficit/Hyperactivity Disorder
  3. NCT00937040 - A Placebo Controlled Double-Blind, Parallel Group, Individualizing Dosing Study Optimizing Treatment of Adults With Attention Deficit Hyperactivity Disorder to an Effective Response With OROS Methylphenidate
  4. NCT00326391 - A Placebo-Controlled, Double-Blind, Parallel-Group, Dose-Titration Study to Evaluate the Efficacy and Safety of CONCERTA (Methylphenidate HCl) Extended-release Tablets in Adults With Attention Deficit Hyperactivity Disorder at Doses of 36 mg, 54 mg, 72 mg, 90 mg, or 108 mg Per Day
  5. NCT01323192 - 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
What type of data are you looking for?: Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Request Clinical Trials

Data Request Status

Status: Approved Pending DUA Signature

Research Proposal

Project Title: PATIENCE Individual Patient DATa Network Meta-AnalysIs of the Efficacy aNd aCceptability of ADHD mEdication

Scientific Abstract: BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disorder which has been estimated to affect 5-7% of children and 2.5% of adults worldwide. It is characterised by inattention and/or hyperactivity-impulsivity, that lead to impairment in the family, classrooms, workplaces and social settings and can prevent individuals from maintaining relationships and reaching their full potential. A number of pharmacologic and non-pharmacologic treatments are available. A network meta-analysis (NMA) of published and unpublished data provided the most comprehensive evidence on the relative efficacy, safety and tolerability of pharmacological treatments across the lifespan at the group level.
NMA is a statistical method that uses data from multiple randomised controlled trials (RCTs) and aims to estimate the relative effects of all available treatments for a certain condition. Common applications of NMA use aggregate data and estimate average treatment effects, i.e., treatment effects at the group level. The analysis of aggregate data, although useful on its own, cannot estimate treatment effects for specific types of patients, i.e., it cannot account for important differences in characteristics of the patients that may significantly moderate treatment effects (e.g., previous exposure to medication, specific comorbidities and severity of symptoms).
OBJECTIVE: The overall objective of the IPD-NMA is the establishment of a hierarchy of the efficacy and acceptability/tolerability of the available pharmacological treatments for ADHD in sub-groups of children, adolescents or adults with particular clinical characteristics (see https://esm.ispm.unibe.ch/shinies/iCBT/ for an example). This will provide evidence to inform future clinical guidelines, support personalised approaches in the treatment of ADHD, and identify areas for further research.
DESIGN: An Individual Patient Data Network Meta-Analysis (IPD-NMA)
PARTICIPANTS: This NMA has been designed to use data from individual participants recruited in RCTs of ADHD medications, to enable more detailed and flexible analyses to be performed than those based on aggregate data.
MAIN OUTCOME MEASURES: Primary outcome is effacacy, secondary outcomes included tolerability and acceptability
STATISTICAL ANALYSIS: Two stage IPD-NMA

Brief Project Background and Statement of Project Significance: For ADHD, as for many other disorders, clinicians, patients and parents/carers are faced with a range of possible medications to choose from. In the absence of evidence-based biomarkers and clinical predictors of response or adverse effects, currently treatment selection in the clinical practice often relies on trial-and-error [7]. This results in protracted periods of sub-optimal treatment until the drug that provides the optimum balance of efficacy and tolerability for an individual patient is identified.
The most robust empirical evidence for the effects of treatments comes from RCTs. The results of these can be statistically combined in pairwise Meta-Analyses (MAs) to estimate the relative effects of two drugs (or one drug and placebo). When a range of treatment options are available, the utility of pairwise comparisons from MAs for selection of treatment is limited. NMAs overcome this limitation by comparing multiple treatments simultaneously in a single analysis to produce estimates of relative effects among all interventions [11]. In addition to including data on treatment comparisons provided directly from RCTs, NMAs also incorporate indirect information, obtained by combining data from RCTs of different drugs evaluated against the same comparator. NMAs facilitate initial treatment selection by providing estimates of the extent to which patients with a clinical condition are likely to benefit from and/or not be harmed by a particular medication, as compared to a reference treatment (e.g., active drug or placebo).
However, NMA results based on aggregate data only estimate the average treatment effects, i.e., at the group level. Individual Patient Data Network Meta-Analyses (IPD-NMAs) analyse patient-level rather that group-level data. This type of analysis also has the capacity to provide estimates of the effects of medication on subgroups of patients with specific characteristics, such as previous exposure to medications, severity of symptoms and comorbidities which could moderate the effects of medications [12]. Additionally, an IPD-NMA has other advantages as compared to aggregate data NMAs, such as the ability to standardise analyses across different studies, harmonise the definition of outcomes, offset inadequate reporting of individual studies, and allow more accurate assessment of study quality [13] [14].
Data for IPD-NMAs may be obtained from trial investigators and sponsors, including pharmaceutical companies, either directly or via data-sharing repositories and platforms. In addition to published data, the datasets may include information on unreported outcomes, allowing additional comparisons to be made. The data collected are first re-examined and verified, and any discrepancies found are checked with authors. As well as re-analyses of data reported in trial publications, additional analyses can be performed on data not reported in trial publications, new hypotheses can be tested and data from participants excluded from the original analyses can be included [15].

Specific Aims of the Project: AIM: To collect and analyse individual patient data (IPD) from published and unpublished RCTs of FDA-approved pharmacological treatments for ADHD in children, adolescents, and/or adults.
The overall objective of the IPD-NMA is the establishment of a hierarchy of the efficacy and acceptability/tolerability of the available pharmacological treatments for ADHD in sub-groups of children, adolescents or adults with particular clinical characteristics (see https://esm.ispm.unibe.ch/shinies/iCBT/ for an example). This will involve:
i. assessing pharmacological treatments in patients with ADHD, in terms of:
? overall efficacy on severity of ADHD core symptoms;
? tolerability (defined as dropouts due to adverse events);
? acceptability (defined as dropouts due to any cause);
? changes in body weight changes in blood pressure and in heart rate
? individual adverse events
ii. exploring interactions between treatment effects and potential moderators, to identify predictors of individual response to medication.
iii. re-analysing data to reduce clinical heterogeneity and network inconsistency, by controlling for any patient-level moderators identified.
iv. using the data to conduct time-to-event analyses.
v. IPD may include additional information compared to aggregate data

Study Design: Meta-analysis (analysis of multiple trials together)

What is the purpose of the analysis being proposed? Please select all that apply.:

Software Used:

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: The selection of studies for the IPD-NMA follows the same criteria as in our previously published an aggregate data NMA [16], with the addition of including the newly FDA approved ADHD medication ? Viloxazine. The same study selection, inclusion and exclusion criteria are being used for this IPD NMA will be published in the study protocol.
Participants will include children (aged ?5 years and

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: Primary outcome
Observer-rated efficacy: the endpoint scores for the severity of core symptoms of ADHD (combined score of inattention, hyperactivity and impulsivity) measured on validated ADHD rating scales will be used. For adults, efficacy will be based on ratings by clinicians and, for children and adolescents, ratings by clinicians and teachers will be used.
In the first instance we aim to use the same rating scale for all studies. If studies do not report scores from the same rating scale, we will convert different scores into one single score from the same rating scale, ADHD-RS, as done by Furukawa et al [18]. In order to convert, we need studies reporting scores from different rating scales for the same outcome. If we are able to convert everything into ADHD-RS, we will synthesise data as a mean difference.
If we are not able to convert different scales into ADHD-RS, we will keep the original scales and will do the analysis using the standardized mean difference. We will use the following hierarchy:
1. ADHD-RS
2. SNAP;
3. Conner?s rating scale (any version);
4. Other ADHD scales (e.g., SKAMP, PERMP).

Main Predictor/Independent Variable and how it will be categorized/defined for your study: Please see table 1 attached for list of variables of interest

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: Please see table 1 attached for list of variables of interest

Statistical Analysis Plan: Our aim is to estimate the patient-specific effectiveness and safety of each drug, to facilitate a personalised choice of treatment. Our main hypothesis is that routinely collected patient-level covariates (such as disease and medication history, demographics, etc.) can be used to predict to some extent the effects of the various drugs on the outcomes of the individual patients, to guide a personalized choice of treatment. The overall target of our analysis is to estimate relative effects between all interventions with respect to [19] the endpoint ADHD symptom score, [20] the probability of dropping out of treatment and [21] the probability of specific side effects for a patient with certain characteristics after 12 weeks of treatment. To maximize the power to detect modifications of the drug effects according to patient characteristics, we opt to synthesise all data that becomes available. Consequently, our sample size will be the maximum available, subject to the responses from data owners. In cases of treatment switch during the randomised phase, patients will be analysed according to the drug they were initially allocated, i.e., following an intention-to-treat analysis.
It is very often the case that for some patients we do not have complete information on all the covariates of interest, or the outcomes. Analysing only patients with complete records will reduce our statistical power and may bias results, when outcomes are not missing completely at random. To retain patients with missing data in our analyses, we will create 10 multiply imputed datasets, while taking into account the stratification of patients in studies. This will be based on the missing at random assumption. We will use all covariates and outcomes for our imputation models. For implementation, we will use the jomo package in R [22].
Individual patient data network meta-analyses
We will employ a two stage IPD-NMA. At the first stage, we will analyse each study separately, using the 10 multiply imputed datasets [28]. We will fit a Bayesian model including linear interactions between treatment and a list of predefined suspected effect modifiers. These include: age, gender, weight, height, baseline ADHD score, comorbid psychiatric disorder and baseline quality of life score. For binary covariates we will use a binomial likelihood, or, if deemed possible according to data availability, we will do a time-to-event analysis. To avoid overfitting and aiming to have better generalizability of our model, we will use a Bayesian LASSO prior distribution for all treatment-covariate interactions [29]. Next, the posterior estimates from all multiply imputed datasets will be combined into a final posterior distribution [30]. Using this distribution, we will summarize the study-specific estimates for average treatment effect and treatment-covariate interactions as well as their variance-covariance matrix from each study. Finally, we will synthesize the study-specific point estimates and variance covariance matrices across the whole network assuming consistency, using multivariate normal distributions. As in the analysis of aggregate data, we will assume a common heterogeneity parameter across the whole network. The final output of this analysis will be estimates of relative effects and 95% Credible Intervals for all treatment comparisons, for all levels of the effect modifiers. To facilitate the uptake of our IPD NMA results, we will build an online tool (for an example see https://esm.ispm.unibe.ch/shinies/iCBT/), where the estimates of our models will be used in conjunction with input regarding baseline patient characteristics to estimate patient-level relative effects regarding all interventions in the network.

Narrative Summary: The analysis of aggregate data, although useful on its own, cannot estimate treatment effects for specific types of patients, i.e., it cannot account for important differences in characteristics of the patients that may significantly moderate treatment effects. PATIENCE, an Individual Patient Data Network Meta-Analysis (IPD-NMA), has been designed to use data from individual participants recruited in RCTs of ADHD medications, to enable more detailed and flexible analyses to be performed than those based on aggregate data. This will provide evidence to inform future clinical guidelines, support personalised approaches in the treatment of ADHD, and identify areas for further research.

Project Timeline: 12-36 months
Project start date: 01.01.2023
Data harmonisation: 6-12 months
Data analysis: 6 months
Data results reported back to YODA within 12 months of analysis completion

Dissemination Plan: Publication of the results from each phase of the project in peer-reviewed, scientific journals and dissemination of the findings to the general audience via patient and public engagement. Potential journals include - Lancet Psychiatry, Lancet, JAMA.
We will ensure that the data providers are notified and have the opportunity to view the results before publication.

Bibliography:

1. Faraone, S.V., et al., Attention-deficit/hyperactivity disorder. Nature Reviews Disease Primers, 2015. 1(1): p. 15020.
2. Polanczyk, G.V., et al., ADHD prevalence estimates across three decades: an updated systematic review and meta-regression analysis. International Journal of Epidemiology, 2014. 43(2): p. 434-442.
3. Simon, V., et al., Prevalence and correlates of adult attention-deficit hyperactivity disorder: meta-analysis. British Journal of Psychiatry, 2009. 194(3): p. 204-211.
4. Lara, C., et al., Childhood Predictors of Adult Attention-Deficit/Hyperactivity Disorder: Results from the World Health Organization World Mental Health Survey Initiative. Biological Psychiatry, 2009. 65(1): p. 46-54.
5. Wolraich, M.L., et al., Clinical Practice Guideline for the Diagnosis, Evaluation, and Treatment of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. Pediatrics, 2019. 144(4): p. e20192528.
6. (NICE), N.I.f.H.a.C.E., Attention deficit hyperactivity disorder: diagnosis and management, NICE guideline [NG87. 2019 Published 14 March 2018. Last update 13 September 2019.)
7. Cortese, S., Pharmacologic Treatment of Attention Deficit-Hyperactivity Disorder. N Engl J Med, 2020. 383(11): p. 1050-1056.
8. Chai, G., et al., Trends of Outpatient Prescription Drug Utilization in US Children, 2002?2010. Pediatrics, 2012. 130(1): p. 23.
9. Renoux, C., et al., Prescribing trends of attention-deficit hyperactivity disorder (ADHD) medications in UK primary care, 1995-2015. Br J Clin Pharmacol, 2016. 82(3): p. 858-68.
10. Cortese, S., et al., Comparative efficacy and tolerability of medications for attention-deficit hyperactivity disorder in children, adolescents, and adults: a systematic review and network meta-analysis. Lancet Psychiatry, 2018. 5(9): p. 727-738.
11. Cipriani, A., et al., Conceptual and Technical Challenges in Network Meta-analysis. Annals of Internal Medicine, 2013. 159(2): p. 130-137.
12. Veroniki, A.A., et al., Methods and characteristics of published network meta-analyses using individual patient data: protocol for a scoping review. BMJ Open, 2015. 5(4): p. e007103.
13. Debray, T.P.A., et al., Individual Participant Data (IPD) Meta-analyses of Diagnostic and Prognostic Modeling Studies: Guidance on Their Use. PLOS Medicine, 2015. 12(10): p. e1001886.
14. Tierney JF, S.L., Clarke M. Individual participant data., Individual participant data. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.0 (updated July 2019). Cochrane, 2019, 2019. Chapter 26.
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