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  ["project_date_type"]=>
  string(91) "Individual Participant-Level Data, which includes Full CSR and all supporting documentation"
  ["property_scientific_abstract"]=>
  string(2586) "Background:

Rheumatoid arthritis (RA) is a long-term autoimmune disease which is characterised by swelling, stiffness, and pain in the joints. The cause of this disease where immune cells attack one?s own cells is not well known. Epidemiological studies has clearly shown a gender bias with women being three-four times more likely to develop RA in their lifetime[1]. Since the cause of RA is not clear, the treatment is focussed on managing symptoms. Most anti-inflammatory drugs reduce the sensation of pain but do not modify the disease itself. Disease modifying therapies aim to dampen the overactivated immune system and are categorised as immunosuppressants, the most prescribed immunosuppressant being methotrexate (MTX). Advances in monoclonal antibodies (mAbs) and chimeric mAbs has broadened the druggable targets and this has led to development of anti tumour necrosis factor alpha (TNF-?) (e.g., Adalimumab [2?4], Golimumab [5?7], infliximab [8,9]), anti interlukin 6 (IL6) (e.g., Sirukumamb [10?13]), and anti-IL23 (e.g., guselkumab [14]) and dual IL12/IL23 targeting antibodies (e.g., ustekinumab [14]) which have completed several phase II/III clinical trials as primary therapy or as a secondary therapy in patients who have already received/are receiving disease-modifying antirheumatic drug (DMARD) therapy.

Since, the triggers/causes of disease are not clear, more epidemiological studies are necessary to understand the role of various factors including environmental as well as genetic risk factors. Additionally, the differences in response to therapies could potentially result in molecular differences in the disease pathology. 

Objective: The main objective of this study is to pore into the rich randomised clinical trial (RCT) data and identify potential correlations between patient demographics as well as environmental factors and effectiveness of different RA treatments. 

Study Design:  Meta-anlysis of published RCT

Participants: Adults (>18 years) with Rheumatoid arthritis 

Primary and Secondary Outcome Measure(s): Primary: ACR20, Secondary: ACR50

Statistical Analysis: A suite of statistical approaches including network-based correlation analyses and logistical regression analyses and other machine learning algorithms will be used to assess the potential risk factors associated with poor response to therapies. A meta-analysis of data will be performed with random effect model to compare different therapies. More details are provided in the statistical analysis plan section of this data request form."
  ["project_brief_bg"]=>
  string(809) "This project is aimed to identify associations and prognostic markers for treatment with antibody-based treatments to determine the efficacy of different treatments against rheumatoid arthritis. We are also interested to assess any demographics factors that might explain differences in efficacy and safety profile in rheumatoid arthritis patients.

We hope that this analysis will lead to new insights about the effect of the arthritis treatment and their dependence on patient characteristics and has the potential to maximise benefit of this drug for rheumatoid arthritis in the future.

Recent work has compared different antibodies using network-based approach [15]. Our work will aim to identify any associations between patient demographics and outcome when treated with different antibodies."
  ["project_specific_aims"]=>
  string(1177) "Hypotheses:

1)	The level of activation of pro-inflammatory factors varies across patients and hence, the response to the same dose of an antibody may be difference across patients with RA

2)	Antibodies targeting different pro-inflammatory factors in the same pathway may have different distribution profile and ability to neutralise or inhibit the activity of aberrant immune activation and off-target interactions. This can influence the response and safety and therefore, some patients subgroups may have a better response to one treatment vs the other

3)	By combining multiple clinical trials, we can potentially do these comparisons and identify relationships between the response to a therapy as well as safety profile and patient characteristics as well as mode of delivery

Aims and objectives:

1)	To assess whether factors such as gender, age, location, BMI, disease duration, prior treatment, ethnicity, smoking, mode of delivery, etc. impacts the response to antibody-based therapies

2)	Identify potential prognostic markers which can enable choosing the right antibody-based therapy and pave the way for more personalised medicine."
  ["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(8) {
    [0]=>
    array(2) {
      ["value"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(49) "New research question to examine treatment safety"
      ["label"]=>
      string(49) "New research question to examine treatment safety"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(76) "Confirm or validate previously conducted research on treatment effectiveness"
      ["label"]=>
      string(76) "Confirm or validate previously conducted research on treatment effectiveness"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(69) "Confirm or validate previously conducted research on treatment safety"
      ["label"]=>
      string(69) "Confirm or validate previously conducted research on treatment safety"
    }
    [4]=>
    array(2) {
      ["value"]=>
      string(36) "Participant-level data meta-analysis"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [5]=>
    array(2) {
      ["value"]=>
      string(69) "Meta-analysis using data from the YODA Project and other data sources"
      ["label"]=>
      string(69) "Meta-analysis using data from the YODA Project and other data sources"
    }
    [6]=>
    array(2) {
      ["value"]=>
      string(37) "Develop or refine statistical methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [7]=>
    array(2) {
      ["value"]=>
      string(50) "Research on clinical prediction or risk prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_purposes_exp"]=>
  string(0) ""
  ["project_software_used"]=>
  array(2) {
    ["value"]=>
    string(6) "Python"
    ["label"]=>
    string(6) "Python"
  }
  ["project_software_used_exp"]=>
  string(0) ""
  ["project_research_methods"]=>
  string(642) "We will be conducting analysis on trials provided on the Yoda website (no external sources beyond Yoda). Any other aggregate data on the trials listed on the Yoda website will be obtained from links on each Yoda trial page, e.g. from ClinicalTrials.gov, and trial-associated publications.

Inclusion criteria:

-	We will be conducting our analyses on randomly controlled trials, controlled with a placebo group.

-	Age of patients >= 18 years

-	Patients have rheumatoid arthritis

Exclusion criteria:

-	Trials with no subgroup details on age, gender, country, etc. will be excluded from subgroup level analyses"
  ["project_main_outcome_measure"]=>
  string(352) "Primary: ACR20 ? defined as a >20% improvement in the number of tender and number of swollen joints and in 3 of the following 5 criteria: patient global assessment, physician global assessment, functional ability measure, visual analog pain scale and erythrocyte sedimentation rate or C-reactive protein (CRP)

Secondary: ACR50 (as above but >50%)"
  ["project_main_predictor_indep"]=>
  string(6) "-	Age:"
  ["project_other_variables_interest"]=>
  string(67) "Previous / current other drugs used. E.g. MTX, anti TNF alpha, etc."
  ["project_stat_analysis_plan"]=>
  string(1367) "-	Descriptive statistics will be used to assess bivariate relationships between single independent variable (age, gender, etc.) and primary/secondary outcome. Log ratios will be evaluated using logistical regression analyses

-	Multivariable analyses (classification models, analysis of variance (ANOVA), clustering, etc.) will be performed by combining multiple independent variables.

-	A random effect models will be used to conduct meta-analysis19 as the objective is to potentially use the results beyond the existing clinical trials

-	When conducting meta-analysis a three stage approach will be used:

       o	Cochrane risk-of-bias tool: ROB216 will be used to identify any discrepancies or biases in the clinical trials. Although studies will not be excluding despite having high risk of bias, but these will be highlighted when discussing the analyses and methods to account for bias will be applied if time permits17,18.

      o	A summary statistic will be calculated for each study, to describe the observed intervention effect in the same way for every study. Since our outcomes are ACR20 and ACR50 (discrete variables), risk ratio will be used for analysis.

     o	A summary (combined) intervention effect estimate will be calculated as a weighted average of the intervention effects estimated in the individual studies."
  ["project_timeline"]=>
  string(190) "Data gathering and method selection: October-November 2022

Data analysis and interpretation: November 2022-July 2023

Data summary, visualisation and report submission: Sept. 2023"
  ["project_dissemination_plan"]=>
  string(303) "The findings will be discussed within the department as well as a project report will be submitted at the end of the project. Additionally, any novel insights will be shared with the wider scientific community in the form of a journal publication and by presentations or posters in academic conferences."
  ["project_bibliography"]=>
  string(6512) "
References

1.	Ngo, S. T.; Steyn, F. J.; McCombe, P. A. Gender Differences in Autoimmune Disease. Front. Neuroendocrinol. 2014, 35 (3), 347?369. https://doi.org/10.1016/j.yfrne.2014.04.004.

2.	Weinblatt, M. E.; Keystone, E. C.; Furst, D. E.; Moreland, L. W.; Weisman, M. H.; Birbara, C. A.; Teoh, L. A.; Fischkoff, S. A.; Chartash, E. K. Adalimumab, a Fully Human Anti-Tumor Necrosis Factor ? Monoclonal Antibody, for the Treatment of Rheumatoid Arthritis in Patients Taking Concomitant Methotrexate: The ARMADA Trial. Arthritis Rheum. 2003, 48 (1), 35?45. https://doi.org/10.1002/art.10697.

3.	Bartelds, G. M.; Wijbrandts, C. A.; Nurmohamed, M. T.; Stapel, S.; Lems, W. F.; Aarden, L.; Dijkmans, B. A. C.; Tak, P. P.; Wolbink, G. J. Clinical Response to Adalimumab: Relationship to Anti-Adalimumab Antibodies and Serum Adalimumab Concentrations in Rheumatoid Arthritis. Ann. Rheum. Dis. 2007, 66 (7), 921?926. https://doi.org/10.1136/ard.2006.065615.

4.	Lovell, D. J.; Ruperto, N.; Goodman, S.; Reiff, A.; Jung, L.; Jarosova, K.; Nemcova, D.; Mouy, R.; Sandborg, C.; Bohnsack, J.; Elewaut, D.; Foeldvari, I.; Gerloni, V.; Rovensky, J.; Minden, K.; Vehe, R. K.; Weiner, L. W.; Horneff, G.; Huppertz, H.-I.; Olson, N. Y.; Medich, J. R.; Carcereri-De-Prati, R.; McIlraith, M. J.; Giannini, E. H.; Martini, A. Adalimumab with or without Methotrexate in Juvenile Rheumatoid Arthritis. N. Engl. J. Med. 2008, 359 (8), 810?820. https://doi.org/10.1056/nejmoa0706290.

5.	Keystone, E. C.; Genovese, M. C.; Klareskog, L.; Hsia, E. C.; Hall, S. T.; Miranda, P. C.; Pazdur, J.; Bae, S. C.; Palmer, W.; Zrubek, J.; Wiekowski, M.; Visvanathan, S.; Wu, Z.; Rahman, M. U. Golimumab, a Human Antibody to Tumour Necrosis Factor ? given by Monthly Subcutaneous Injections, in Active Rheumatoid Arthritis despite Methotrexate Therapy: The GO-FORWARD Study. Ann. Rheum. Dis. 2009, 68 (6), 789?796. https://doi.org/10.1136/ard.2008.099010.

6.	Smolen, J. S.; Kay, J.; Doyle, M. K.; Landew, R.; Matteson, E. L.; Wollenhaupt, J.; Gaylis, N.; Murphy, F. T.; Neal, J. S.; Zhou, Y.; Visvanathan, S.; Hsia, E. C.; Rahman, M. U. Golimumab in Patients with Active Rheumatoid Arthritis after Treatment with Tumour Necrosis Factor ? Inhibitors (GO-AFTER Study): A Multicentre, Randomised, Double-Blind, Placebo-Controlled, Phase III Trial. Lancet 2009, 374 (9685), 210?221. https://doi.org/10.1016/S0140-6736(09)60506-7.

7.	Singh, J. A.; Noorbaloochi, S.; Singh, G. Golimumab for Rheumatoid Arthritis: A Systematic Review. J. Rheumatol. 2010, 37 (6), 1096?1104. https://doi.org/10.3899/jrheum.091466.

8.	Wolfe, F.; Michaud, K. The Effect of Methotrexate and Anti-Tumor Necrosis Factor Therapy on the Risk of Lymphoma in Rheumatoid Arthritis in 19,562 Patients during 89,710 Person-Years of Observation. Arthritis Rheum. 2007, 56 (5), 1433?1439. https://doi.org/10.1002/art.22579.

9.	St.Clair, E. W.; Van Der Heijde, D. M. F. M.; Smolen, J. S.; Maini, R. N.; Bathon, J. M.; Emery, P.; Keystone, E.; Schiff, M.; Kalden, J. R.; Wang, B.; DeWoody, K.; Weiss, R.; Baker, D. Combination of Infliximab and Methotrexate Therapy for Early Rheumatoid Arthritis: A Randomized, Controlled Trial. Arthritis Rheum. 2004, 50 (11), 3432?3443. https://doi.org/10.1002/art.20568.

10.	Hu, C.; Xu, Y.; Zhuang, Y.; Hsu, B.; Sharma, A.; Xu, Z.; Zhang, L.; Zhou, H. Joint Longitudinal Model Development: Application to Exposure?Response Modeling of ACR and DAS Scores in Rheumatoid Arthritis Patients Treated with Sirukumab. J. Pharmacokinet. Pharmacodyn. 2018, 45 (5), 679?691. https://doi.org/10.1007/s10928-018-9598-5.

11.	Takeuchi, T.; Thorne, C.; Karpouzas, G.; Sheng, S.; Xu, W.; Rao, R.; Fei, K.; Hsu, B.; Tak, P. P. Sirukumab for Rheumatoid Arthritis: The Phase III SIRROUND-D Study. Ann. Rheum. Dis. 2017, 76 (12), 2001?2008. https://doi.org/10.1136/annrheumdis-2017-211328.

12.	Aletaha, D.; Bingham, C. O.; Tanaka, Y.; Agarwal, P.; Kurrasch, R.; Tak, P. P.; Popik, S. Efficacy and Safety of Sirukumab in Patients with Active Rheumatoid Arthritis Refractory to Anti-TNF Therapy (SIRROUND-T): A Randomised, Double-Blind, Placebo-Controlled, Parallel-Group, Multinational, Phase 3 Study. Lancet 2017, 389 (10075), 1206?1217. https://doi.org/10.1016/S0140-6736(17)30401-4.

13.	Aletaha, D.; Bingham, C. O.; Karpouzas, G. A.; Takeuchi, T.; Thorne, C.; Bili, A.; Agarwal, P.; Hsu, B.; Rao, R.; Brown, K.; Tanaka, Y. Long-Term Safety and Efficacy of Sirukumab for Patients with Rheumatoid Arthritis Who Previously Received Sirukumab in Randomised Controlled Trials (SIRROUND-LTE). RMD Open 2021, 7 (1). https://doi.org/10.1136/rmdopen-2020-001465.

14.	Smolen, J. S.; Agarwal, S. K.; Ilivanova, E.; Xu, X. L.; Miao, Y.; Zhuang, Y.; Nnane, I.; Radziszewski, W.; Greenspan, A.; Beutler, A.; Baker, D. A Randomised Phase II Study Evaluating the Efficacy and Safety of Subcutaneously Administered Ustekinumab and Guselkumab in Patients with Active Rheumatoid Arthritis despite Treatment with Methotrexate. Ann. Rheum. Dis. 2017, 76 (5), 831?839. https://doi.org/10.1136/annrheumdis-2016-209831.

15.	Janke, K.; Biester, K.; Krause, D.; Richter, B.; Schrmann, C.; Hirsch, K.; Hrn, H.; Kerekes, M. F.; Kohlepp, P.; Wieseler, B. Comparative Effectiveness of Biological Medicines in Rheumatoid Arthritis: Systematic Review and Network Meta-Analysis Including Aggregate Results from Reanalysed Individual Patient Data. BMJ 2020, 370, 1?11. https://doi.org/10.1136/bmj.m2288.

16.	Higgins, J. P. T.; Altman, D. G.; Gtzsche, P. C.; Jni, P.; Moher, D.; Oxman, A. D.; Savovi?, J.; Schulz, K. F.; Weeks, L.; Sterne, J. A. C. The Cochrane Collaboration?s Tool for Assessing Risk of Bias in Randomised Trials. BMJ 2011, 343 (7829), 1?9. https://doi.org/10.1136/bmj.d5928.

17.	Thompson, S.; Ekelund, U.; Jebb, S.; Lindroos, A. K.; Mander, A.; Sharp, S.; Turner, R.; Wilks, D. A Proposed Method of Bias Adjustment for Meta-Analyses of Published Observational Studies. Int. J. Epidemiol. 2011, 40 (3), 765?777. https://doi.org/10.1093/ije/dyq248.

18.	Doi, S. A. R.; Thalib, L. A Quality-Effects Model for Meta-Analysis. Epidemiology 2008, 19 (1), 94?100. https://doi.org/10.1097/EDE.0b013e31815c24e7.

19.	Tufanaru, C.; Munn, Z.; Stephenson, M.; Aromataris, E. Fixed or Random Effects Meta-Analysis? Common Methodological Issues in Systematic Reviews of Effectiveness. Int. J. Evid. Based. Healthc. 2015, 13 (3), 196?207. https://doi.org/10.1097/XEB.0000000000000065.
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