DOI: 10.25934/00005276
title: Predicting the Treatment Response of Certolizumab for Individual Patients with Rheumatoid Arthritis: An Individual Participant Data Meta-Analysis - Luo

description: Rheumatoid arthritis (RA) is a chronic inflammatory disease, for which we cannot currently expect complete cure. The drugs that can slow the disease progression are known as disease modifying anti-rheumatic drugs (DMARDs), which have many types. Most RA patients experience long-term treatment. According to the strategy proposed by the practice guideline, a physician should select an intervention for a patient, then re-evaluate after 3 to 6 months, based on which a next-step strategy is given. In order to improve long-term prognosis of RA (such as delaying the progression of bone fusion or functional deficiency), we should assure 3-6 months short-term clinical outcome primarily. To achieve this goal, physicians should select the optimal intervention based on 3 to 6 months clinical response for the patient.

It is not an easy task for doctors to choose the right drug from so many choices. Especially the evidence usually used now is in the form of randomized controlled trials (RCTs) or their meta- analyses (MAs) at the aggregate level, which can only provide average results. But patients' responses differ, based on their particular genetic, biologic, and clinical features. Thus it is highly necessary to identify the subgroups of patients who show different responses, to build a prediction model of treatment response that depends on the individual characteristics, and further help physicians personalize treatment strategy.

For this purpose, we decide to use the individual participant data meta-analysis (IPD-MA) of RCTs, which has recently been recommended as a proper approach by many articles. It is superior than usual observational study-based prediction models of treatment effects in: (1) RCTs have more rigorously collected data and are more powerful in dealing with missing data; (2) RCTs can be used to predict the relative treatment effect under alternative treatment conditions.Furthermore, IPD-MA, as a collection of several RCTs, can increase the statistical power in dealing with complex models.

As for feasibility, several promising modeling approaches of using IPD-MA to predict treatment response have been established and validated in some simulation studies, yet only one study has applied the method to real clinical settings (depression). The lead researcher of that study is also a co-researcher of our study. Until now no such studies have been conducted for RA.

As a result, we are motivated to develop a prediction model of treatment effects based on individual characteristics of RA patients through IPD-MA. The reason why we choose certolizumab (CTZ) is that TNFa inhibitors are the most classic and widely used biologic DMARDs for RA, with plenty of IPD. We may consider to expand the model to compare several drugs as an IPD-NMA (network meta-analysis) in the future.
The general study process is: (1) search-select-risk of bias assessment of eligible studies; (2) applying for IPD; (3) identify effect modifiers (affect treatment/control group's outcome differently) for the CTZ; (4) developing the prediction model of treatment effects, including both effect modifiers and prognostic factors (affect the outcome in treatment/control group equally).