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  string(137) "Research Data Request: Predictors of exposure, therapeutic and adverse effects to medicines used in the treatment of rheumatoid arthritis"
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  string(594) "Modern treatment options in rheumatoid arthritis are associated with significant variability in treatment response and adverse events. Using the diverse range of data collected from clinical trials, it is possible to develop clinical tools that enable improved prediction of therapeutic and adverse outcomes of patients using medicines in the treatment of rheumatoid arthritis. Being able to identify the expected response and adverse effect profile may enable patients and clinicians to make better decisions regarding whether to commence, continue, discontinue or change dosing of treatments."
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      string(29) "University of South Australia"
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      string(29) "University of South Australia"
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      string(240) "NCT00361335 - A Multicenter, Randomized, Double-blind, Placebo-controlled Trial of Golimumab, a Fully Human Anti-TNFa Monoclonal Antibody, Administered Intravenously, in Subjects with Active Rheumatoid Arthritis Despite Methotrexate Therapy"
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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(2499) "Background: Rheumatoid arthritis is a chronic, inflammatory autoimmune condition. If improperly treated, it can lead to permanent disability.  Contemporary treatment options, such as biological medicines can have variable and unpredictable remission rates with varying rates of adverse events. 
Objective: This project seeks to enable improved prediction of the therapeutic and adverse outcomes of patients using medicines for the treatment of rheumatoid arthritis. Being able to identify the expected response and adverse effect profile may enable patients and clinicians to make better decisions regarding whether to commence, continue, discontinue or change dosing of the available treatments.
Study Design: A pooled observational cohort design will be used to identify baseline and on-treatment predictors of adverse effects and measures of therapeutic response.
Participants: Available data from rheumatoid arthritis patients treated with contemporary therapy options (and relevant comparator arms) will be analysed to identify and validate predictors of the most important adverse effects, and clinical/biological/patient predictors of therapeutic outcomes such as response / progression, quality of life and survival.
Main Outcome measures: Potential predictors of the adverse event or therapeutic outcomes will be screened according to biological and clinical plausibility and empirical evidence based on prior research. Primary outcomes of interest will involve analysing remission, response and progression metrics, such as the disease activity index-28 (DAS28), and if the outcome of interest is an index measure, constituents of that measure will also be used.
Statistical Analysis: Cox-proportional hazard / time-to-event models will be used to assess the association between potential predictors and the time to an adverse effect or response / progression / survival. Associations will be reported primarily as hazard ratios with 95% confidence intervals. The association of potential predictors with binary outcomes will be modelled using logistic regression and will be reported as odds ratios with 95% confidence intervals. Longitudinal analysis (e.g. linear and non-linear mixed effect modelling) will be used to assess the nature and patterns of longitudinal changes of key continuous variables (e.g. drug concentration, immune cell counts, number of swollen and tender joints). R software will be used for data preparation, modelling and output." ["project_brief_bg"]=> string(1421) "In the past two decades, rheumatoid arthritis (RA) has undergone several major shifts in treatment modalities that have led to improved patient outcomes (1, 2). Central to this has been the advent of biological disease-modifying anti-rheumatic drugs (bDMARDs) and the more recent addition of targeted synthetic disease-modifying anti-rheumatic drugs. Despite these advances, remission-rates remain variable and unpredictable ranging from 20 ? 40% with bDMARDs (3, 4) and 19 ? 30% with the tsDMARD tofacitinib (5) in RA. Treatment with bDMARDs also confers significantly different rates of adverse effects depending on the biologic used, leading to further variability (6).
This project seeks to enable improved prediction of the therapeutic and adverse outcomes of patients using medicines for the treatment of rheumatoid arthritis. Being able to identify the expected response and adverse effect profile may enable patients and clinicians to make better decisions regarding whether to commence, continue, discontinue or change dosing of the available treatments.
Available data from rheumatoid arthritis patients treated with contemporary therapy options (and relevant comparator arms) will be analysed to identify and validate predictors of the most important adverse effects, and clinical/biological/patient predictors of therapeutic outcomes such as response / progression, quality of life and survival." ["project_specific_aims"]=> string(997) "Objective: This project seeks to enable improved prediction of the therapeutic and adverse outcomes of patients using medicines for the treatment of rheumatoid arthritis. Being able to identify the expected response and adverse effect profile may enable patients and clinicians to make better decisions regarding whether to commence, continue, discontinue or change dosing of the available treatments.
1. Identify baseline and on-treatment predictors and develop clinical prediction models of the key adverse effects of medicines used in the treatment of rheumatoid arthritis.
2. Identify baseline and on-treatment predictors and develop clinical prediction models of the key therapeutic outcomes (response / progression, quality of life and survival) of medicines used in the treatment of rheumatoid arthritis.
3. Evaluate the heterogeneity between rheumatoid arthritis treatments in the occurrence of adverse effects and therapeutic outcomes according to model predicted risk." ["project_study_design"]=> string(0) "" ["project_study_design_exp"]=> string(0) "" ["project_purposes"]=> array(2) { [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(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" } } ["project_purposes_exp"]=> string(0) "" ["project_software_used"]=> array(2) { ["value"]=> string(1) "R" ["label"]=> string(1) "R" } ["project_software_used_exp"]=> string(0) "" ["project_research_methods"]=> string(1275) "Individual patient data will be access from clinical trials across a number of different contemporary treatment options, bDMARDs and tsDMARDs. As this analysis is predominantly exploratory in nature, inclusion and exclusion criteria will vary upon which predictors and outcomes are being analysed - for example, if remission as defined by the DAS28 is the outcome of interest, patients will be excluded from analysis if they do not possess complete DAS28 scores. Broad inclusion criteria will include patients with a diagnosis of rheumatoid arthritis as per ACR or EULAR criteria.
Further studies to be included and pooled with the requested studies, accessed through Vivli.
NCT01721044
NCT01711359
NCT01721057
NCT01710358
NCT00361335
NCT00264537
NCT00973479
NCT01232569
NCT01194414
NCT01007435
NCT00531817
NCT00106535
NCT00721123
NCT00810199
NCT00720798
NCT00160641
NCT00175877
NCT01519791
NCT00717236
NCT02265705
NCT00195663
NCT01185301
NCT01004432
NCT00870467
NCT00647270
NCT00468546
NCT00443651
NCT00299546
NCT00299104
NCT00269867
NCT00266227
NCT00202852
NCT00195702" ["project_main_outcome_measure"]=> string(1200) "Data are required for the outcomes including response / progression (e.g. for rheumatoid arthritis patients this includes - American College of Rheumatology Response Criteria (e.g. 20 / 50 / 70), EULAR response classification, Disease Activity Score based on 28 Joint Count (DAS28), Simplified Disease Activity Index, Clinical Disease Activity Index Modified Total Sharp Score, quality of life changes (e.g. Health Assessment Questionnaire - Disability Index or modified Health Assessment Questionnaire, Short-Form 36 Health Survey, Rheumatoid Arthritis Quality of Life questionnaire),treatment satisfaction questionnaire, pain, fatigue, survival, dose timings and dose regimens, adverse event outcomes (clinician / patient reported adverse effects defined by grade and sentinel events [e.g. hospitalization / discontinuation]), and drug exposure (concentration). Where a metric is derived (e.g. Disease Activity Score-28 Joint Count or Health Assessment Questionnaire - Disability Index) the data required to calculate the score will be required (e.g. 28 Tender Joint Count, 28 Swollen Joint Count, Patient Global assessment of disease activity, Physician global assessment of disease activity etc)" ["project_main_predictor_indep"]=> string(1206) "Potential predictors of the adverse event or therapeutic outcomes will be screened according to biological and clinical plausibility and empirical evidence based on prior research. While exploratory univariate analyses will be conducted, a focus will also be the development of multivariable models that can be developed into clinical prediction tools. As most of the data commonly collected within a clinical trial contains some information on the immune system, disease severity and prognosis, toxicity risk or drug exposure, it is important to have access to all the baseline and follow-up clinical/biological/patient characteristic data collected on an individual for any given study. Covariates to be explored include, but not limited to:
? Baseline values:
o Basic patient characteristics ? e.g. age, sex, race / ethnicity, body-mass index, weight, etc.
o Laboratory data
o Disease classification data
o Other common predicotrs (e.g. concomitant medications, comorbidities etc.)
? Post-baseline values: Post-baseline values (including longitudinal relationships/patterns) can be useful early markers of response / progression, survival, exposure or adverse events." ["project_other_variables_interest"]=> string(9) "As above." ["project_stat_analysis_plan"]=> string(3743) "Cox-proportional hazard / time-to-event models will be used to assess the association between potential predictors and the time to an adverse effect or response / progression / survival. Associations will be reported primarily as hazard ratios with 95% confidence intervals. The association of potential predictors with binary outcomes (e.g. yes / no) will be modelled using logistic regression and will be reported as odds ratios with 95% confidence intervals. Longitudinal analysis (e.g. linear and non-linear mixed effect modelling) will be used to assess the nature and patterns of longitudinal changes of key continuous variables (e.g. drug concentration, immune cell counts, number of swollen and tender joints).
IPD will be conducted through the Vivli platform on the secure server provided.
Software:
The R Software (R Core Team) will be used for data preparation, modelling and graphical output. Non-linear mixed effect modelling of concentration and disease activity measures will be evaluated using the ?R? Software (R Core Team) with Rtools (an Rtoolset), and the R packages ?RxODE?, ?nlmixr?, ?pmetrics? and ?mrgsolve?, ?datatable?, ?dplyr?, ?plyr ?, ?survival?, ?ggplot2?.
Covariate analyses:
Potential predictors will be prioritised according to biological/clinical plausibility and prior evidence of association with the relevant outcome (adverse events, therapeutic response, drug exposure). Should multiple values of a covariate be recorded multiple times for a single visit (e.g. blood pressure) the mean of the multiple reads taken at each visit will be used. Crude associations will be reported based on univariate analysis (adjusting only for the clinical trial and where appropriate the medicines used), and adjusted associations based on a multivariable analysis. Continuous variables will be assessed for non-linearity of association with the outcome using restricted cubic splines. Clinical prediction models will be developed using multivariable analysis and will generally include all available known baseline predictors of the outcome of interest as well as covariates identified in univariate analysis. Penalised models will be used to minimise risk of overfitting. Early markers of exposure, response and toxicity will be primarily evaluated using a landmark approach where possible, with sensitivity analyses based on the use of time-dependent covariates. Landmark time will be dependent on the time points available in individual studies, and the time frame of changes in each specific predictor variable. As this analysis is primarily hypothesis generating and will require subsequent validation of any findings, no formal adjustment for multiple testing is intended. However, this limitation will be clearly stated in any publications of results. As it is expected that < 5% of data will be missing for any variable a complete case analysis is planned. Should variables with substantial missing data be present, the pattern and likely cause of the missing data will be evaluated and if missing at random is reasonable to assume then single regression imputation will be undertaken. Analyses will include evaluating predictors of toxicity incidence and response profiles for relevant comparator medicines. Analyses will also include evaluating the heterogeneity in toxicity incidence and response profiles according to model risk for the specific bDMARD/tsDMARD as compared to relevant comparator arms (e.g. methotrexate). Such analyses will allow a better understanding of the benefits of the bDMARD/tsDMARD, and whether the relationships identified are specific to the bDMARD/tsDMARD being analysed, the comparator medicine or are common across rheumatoid arthritis patients." ["project_timeline"]=> string(339) "Anticipated data access will approximately December or January. Data analysis (along with the relevant checks) will be estimated to be completed 6-8 months after access if granted. Draft manuscript will likely be completed 10-12 months after data access if granted, with publication taking place once authors are happy with the manuscript." ["project_dissemination_plan"]=> string(446) "A summary of the proposed research plan will be posted publicly immediately following acceptance of the research proposal. Results of all completed analyses will be published in peer-reviewed international publications and where possible also presented at scientific meetings. Manuscript(s) will be targeted primarily to international rheumatology journals and will be submitted as soon as possible following completion of the requisite analyses." ["project_bibliography"]=> string(1087) "

1. Vivar N, Van Vollenhoven RF. Advances in the treatment of rheumatoid arthritis. F1000Prime Rep. 2014;6:31-.
2. Burmester GR, Pope JE. Novel treatment strategies in rheumatoid arthritis. The Lancet. 2017;389(10086):2338-48.
3. Romo VC, Vital EM, Fonseca JE, Buch MH. Right drug, right patient, right time: aspiration or future promise for biologics in rheumatoid arthritis? Arthritis Res Ther. 2017;19(1):239.
4. Nagy G, van Vollenhoven RF. Sustained biologic-free and drug-free remission in rheumatoid arthritis, where are we now? Arthritis Res Ther. 2015;17(1):181.
5. Smolen JS, Aletaha D, Gruben D, Zwillich SH, Krishnaswami S, Mebus C. Brief Report: Remission Rates With Tofacitinib Treatment in Rheumatoid Arthritis: A Comparison of Various Remission Criteria. Arthritis & rheumatology (Hoboken, NJ). 2017;69(4):728-34.
6. Singh JA, Wells GA, Christensen R, Tanjong Ghogomu E, Maxwell LJ, MacDonald JK, et al. Adverse effects of biologics: a network meta?analysis and Cochrane overview. Cochrane Database of Systematic Reviews. 2011(2).

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2021-4828

Research Proposal

Project Title: Research Data Request: Predictors of exposure, therapeutic and adverse effects to medicines used in the treatment of rheumatoid arthritis

Scientific Abstract: Background: Rheumatoid arthritis is a chronic, inflammatory autoimmune condition. If improperly treated, it can lead to permanent disability. Contemporary treatment options, such as biological medicines can have variable and unpredictable remission rates with varying rates of adverse events.
Objective: This project seeks to enable improved prediction of the therapeutic and adverse outcomes of patients using medicines for the treatment of rheumatoid arthritis. Being able to identify the expected response and adverse effect profile may enable patients and clinicians to make better decisions regarding whether to commence, continue, discontinue or change dosing of the available treatments.
Study Design: A pooled observational cohort design will be used to identify baseline and on-treatment predictors of adverse effects and measures of therapeutic response.
Participants: Available data from rheumatoid arthritis patients treated with contemporary therapy options (and relevant comparator arms) will be analysed to identify and validate predictors of the most important adverse effects, and clinical/biological/patient predictors of therapeutic outcomes such as response / progression, quality of life and survival.
Main Outcome measures: Potential predictors of the adverse event or therapeutic outcomes will be screened according to biological and clinical plausibility and empirical evidence based on prior research. Primary outcomes of interest will involve analysing remission, response and progression metrics, such as the disease activity index-28 (DAS28), and if the outcome of interest is an index measure, constituents of that measure will also be used.
Statistical Analysis: Cox-proportional hazard / time-to-event models will be used to assess the association between potential predictors and the time to an adverse effect or response / progression / survival. Associations will be reported primarily as hazard ratios with 95% confidence intervals. The association of potential predictors with binary outcomes will be modelled using logistic regression and will be reported as odds ratios with 95% confidence intervals. Longitudinal analysis (e.g. linear and non-linear mixed effect modelling) will be used to assess the nature and patterns of longitudinal changes of key continuous variables (e.g. drug concentration, immune cell counts, number of swollen and tender joints). R software will be used for data preparation, modelling and output.

Brief Project Background and Statement of Project Significance: In the past two decades, rheumatoid arthritis (RA) has undergone several major shifts in treatment modalities that have led to improved patient outcomes (1, 2). Central to this has been the advent of biological disease-modifying anti-rheumatic drugs (bDMARDs) and the more recent addition of targeted synthetic disease-modifying anti-rheumatic drugs. Despite these advances, remission-rates remain variable and unpredictable ranging from 20 ? 40% with bDMARDs (3, 4) and 19 ? 30% with the tsDMARD tofacitinib (5) in RA. Treatment with bDMARDs also confers significantly different rates of adverse effects depending on the biologic used, leading to further variability (6).
This project seeks to enable improved prediction of the therapeutic and adverse outcomes of patients using medicines for the treatment of rheumatoid arthritis. Being able to identify the expected response and adverse effect profile may enable patients and clinicians to make better decisions regarding whether to commence, continue, discontinue or change dosing of the available treatments.
Available data from rheumatoid arthritis patients treated with contemporary therapy options (and relevant comparator arms) will be analysed to identify and validate predictors of the most important adverse effects, and clinical/biological/patient predictors of therapeutic outcomes such as response / progression, quality of life and survival.

Specific Aims of the Project: Objective: This project seeks to enable improved prediction of the therapeutic and adverse outcomes of patients using medicines for the treatment of rheumatoid arthritis. Being able to identify the expected response and adverse effect profile may enable patients and clinicians to make better decisions regarding whether to commence, continue, discontinue or change dosing of the available treatments.
1. Identify baseline and on-treatment predictors and develop clinical prediction models of the key adverse effects of medicines used in the treatment of rheumatoid arthritis.
2. Identify baseline and on-treatment predictors and develop clinical prediction models of the key therapeutic outcomes (response / progression, quality of life and survival) of medicines used in the treatment of rheumatoid arthritis.
3. Evaluate the heterogeneity between rheumatoid arthritis treatments in the occurrence of adverse effects and therapeutic outcomes according to model predicted risk.

Study Design:

What is the purpose of the analysis being proposed? Please select all that apply.: New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations Meta-analysis using data from the YODA Project and other data sources

Software Used: R

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: Individual patient data will be access from clinical trials across a number of different contemporary treatment options, bDMARDs and tsDMARDs. As this analysis is predominantly exploratory in nature, inclusion and exclusion criteria will vary upon which predictors and outcomes are being analysed - for example, if remission as defined by the DAS28 is the outcome of interest, patients will be excluded from analysis if they do not possess complete DAS28 scores. Broad inclusion criteria will include patients with a diagnosis of rheumatoid arthritis as per ACR or EULAR criteria.
Further studies to be included and pooled with the requested studies, accessed through Vivli.
NCT01721044
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NCT01721057
NCT01710358
NCT00361335
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NCT00973479
NCT01232569
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NCT00106535
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NCT00160641
NCT00175877
NCT01519791
NCT00717236
NCT02265705
NCT00195663
NCT01185301
NCT01004432
NCT00870467
NCT00647270
NCT00468546
NCT00443651
NCT00299546
NCT00299104
NCT00269867
NCT00266227
NCT00202852
NCT00195702

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: Data are required for the outcomes including response / progression (e.g. for rheumatoid arthritis patients this includes - American College of Rheumatology Response Criteria (e.g. 20 / 50 / 70), EULAR response classification, Disease Activity Score based on 28 Joint Count (DAS28), Simplified Disease Activity Index, Clinical Disease Activity Index Modified Total Sharp Score, quality of life changes (e.g. Health Assessment Questionnaire - Disability Index or modified Health Assessment Questionnaire, Short-Form 36 Health Survey, Rheumatoid Arthritis Quality of Life questionnaire),treatment satisfaction questionnaire, pain, fatigue, survival, dose timings and dose regimens, adverse event outcomes (clinician / patient reported adverse effects defined by grade and sentinel events [e.g. hospitalization / discontinuation]), and drug exposure (concentration). Where a metric is derived (e.g. Disease Activity Score-28 Joint Count or Health Assessment Questionnaire - Disability Index) the data required to calculate the score will be required (e.g. 28 Tender Joint Count, 28 Swollen Joint Count, Patient Global assessment of disease activity, Physician global assessment of disease activity etc)

Main Predictor/Independent Variable and how it will be categorized/defined for your study: Potential predictors of the adverse event or therapeutic outcomes will be screened according to biological and clinical plausibility and empirical evidence based on prior research. While exploratory univariate analyses will be conducted, a focus will also be the development of multivariable models that can be developed into clinical prediction tools. As most of the data commonly collected within a clinical trial contains some information on the immune system, disease severity and prognosis, toxicity risk or drug exposure, it is important to have access to all the baseline and follow-up clinical/biological/patient characteristic data collected on an individual for any given study. Covariates to be explored include, but not limited to:
? Baseline values:
o Basic patient characteristics ? e.g. age, sex, race / ethnicity, body-mass index, weight, etc.
o Laboratory data
o Disease classification data
o Other common predicotrs (e.g. concomitant medications, comorbidities etc.)
? Post-baseline values: Post-baseline values (including longitudinal relationships/patterns) can be useful early markers of response / progression, survival, exposure or adverse events.

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

Statistical Analysis Plan: Cox-proportional hazard / time-to-event models will be used to assess the association between potential predictors and the time to an adverse effect or response / progression / survival. Associations will be reported primarily as hazard ratios with 95% confidence intervals. The association of potential predictors with binary outcomes (e.g. yes / no) will be modelled using logistic regression and will be reported as odds ratios with 95% confidence intervals. Longitudinal analysis (e.g. linear and non-linear mixed effect modelling) will be used to assess the nature and patterns of longitudinal changes of key continuous variables (e.g. drug concentration, immune cell counts, number of swollen and tender joints).
IPD will be conducted through the Vivli platform on the secure server provided.
Software:
The R Software (R Core Team) will be used for data preparation, modelling and graphical output. Non-linear mixed effect modelling of concentration and disease activity measures will be evaluated using the ?R? Software (R Core Team) with Rtools (an Rtoolset), and the R packages ?RxODE?, ?nlmixr?, ?pmetrics? and ?mrgsolve?, ?datatable?, ?dplyr?, ?plyr ?, ?survival?, ?ggplot2?.
Covariate analyses:
Potential predictors will be prioritised according to biological/clinical plausibility and prior evidence of association with the relevant outcome (adverse events, therapeutic response, drug exposure). Should multiple values of a covariate be recorded multiple times for a single visit (e.g. blood pressure) the mean of the multiple reads taken at each visit will be used. Crude associations will be reported based on univariate analysis (adjusting only for the clinical trial and where appropriate the medicines used), and adjusted associations based on a multivariable analysis. Continuous variables will be assessed for non-linearity of association with the outcome using restricted cubic splines. Clinical prediction models will be developed using multivariable analysis and will generally include all available known baseline predictors of the outcome of interest as well as covariates identified in univariate analysis. Penalised models will be used to minimise risk of overfitting. Early markers of exposure, response and toxicity will be primarily evaluated using a landmark approach where possible, with sensitivity analyses based on the use of time-dependent covariates. Landmark time will be dependent on the time points available in individual studies, and the time frame of changes in each specific predictor variable. As this analysis is primarily hypothesis generating and will require subsequent validation of any findings, no formal adjustment for multiple testing is intended. However, this limitation will be clearly stated in any publications of results. As it is expected that < 5% of data will be missing for any variable a complete case analysis is planned. Should variables with substantial missing data be present, the pattern and likely cause of the missing data will be evaluated and if missing at random is reasonable to assume then single regression imputation will be undertaken. Analyses will include evaluating predictors of toxicity incidence and response profiles for relevant comparator medicines. Analyses will also include evaluating the heterogeneity in toxicity incidence and response profiles according to model risk for the specific bDMARD/tsDMARD as compared to relevant comparator arms (e.g. methotrexate). Such analyses will allow a better understanding of the benefits of the bDMARD/tsDMARD, and whether the relationships identified are specific to the bDMARD/tsDMARD being analysed, the comparator medicine or are common across rheumatoid arthritis patients.

Narrative Summary: Modern treatment options in rheumatoid arthritis are associated with significant variability in treatment response and adverse events. Using the diverse range of data collected from clinical trials, it is possible to develop clinical tools that enable improved prediction of therapeutic and adverse outcomes of patients using medicines in the treatment of rheumatoid arthritis. Being able to identify the expected response and adverse effect profile may enable patients and clinicians to make better decisions regarding whether to commence, continue, discontinue or change dosing of treatments.

Project Timeline: Anticipated data access will approximately December or January. Data analysis (along with the relevant checks) will be estimated to be completed 6-8 months after access if granted. Draft manuscript will likely be completed 10-12 months after data access if granted, with publication taking place once authors are happy with the manuscript.

Dissemination Plan: A summary of the proposed research plan will be posted publicly immediately following acceptance of the research proposal. Results of all completed analyses will be published in peer-reviewed international publications and where possible also presented at scientific meetings. Manuscript(s) will be targeted primarily to international rheumatology journals and will be submitted as soon as possible following completion of the requisite analyses.

Bibliography:

1. Vivar N, Van Vollenhoven RF. Advances in the treatment of rheumatoid arthritis. F1000Prime Rep. 2014;6:31-.
2. Burmester GR, Pope JE. Novel treatment strategies in rheumatoid arthritis. The Lancet. 2017;389(10086):2338-48.
3. Romo VC, Vital EM, Fonseca JE, Buch MH. Right drug, right patient, right time: aspiration or future promise for biologics in rheumatoid arthritis? Arthritis Res Ther. 2017;19(1):239.
4. Nagy G, van Vollenhoven RF. Sustained biologic-free and drug-free remission in rheumatoid arthritis, where are we now? Arthritis Res Ther. 2015;17(1):181.
5. Smolen JS, Aletaha D, Gruben D, Zwillich SH, Krishnaswami S, Mebus C. Brief Report: Remission Rates With Tofacitinib Treatment in Rheumatoid Arthritis: A Comparison of Various Remission Criteria. Arthritis & rheumatology (Hoboken, NJ). 2017;69(4):728-34.
6. Singh JA, Wells GA, Christensen R, Tanjong Ghogomu E, Maxwell LJ, MacDonald JK, et al. Adverse effects of biologics: a network meta?analysis and Cochrane overview. Cochrane Database of Systematic Reviews. 2011(2).