Abstract
BackgroundA model that can predict treatment response for a patient with specific baseline characteristics would help decision-making in personalized medicine. The aim of the study is to develop such a model in the treatment of rheumatoid arthritis (RA) patients who receive certolizumab (CTZ) plus methotrexate (MTX) therapy, using individual participant data meta-analysis (IPD-MA).MethodsWe will search Cochrane CENTRAL, PubMed, and Scopus as well as clinical trial registries, drug regulatory agency reports, and the pharmaceutical company websites from their inception onwards to obtain randomized controlled trials (RCTs) investigating CTZ plus MTX compared with MTX alone in treating RA. We will request the individual-level data of these trials from an independent platform (http://vivli.org). The primary outcome is efficacy defined as achieving either remission (based on ACR-EULAR Boolean or index-based remission definition) or low disease activity (based on either of the validated composite disease activity measures). The secondary outcomes include ACR50 (50% improvement based on ACR core set variables) and adverse events. We will use a two-stage approach to develop the prediction model. First, we will construct a risk model for the outcomes via logistic regression to estimate the baseline risk scores. We will include baseline demographic, clinical, and biochemical features as covariates for this model. Next, we will develop a meta-regression model for treatment effects, in which the stage 1 risk score will be used both as a prognostic factor and as an effect modifier. We will calculate the probability of having the outcome for a new patient based on the model, which will allow estimation of the absolute and relative treatment effect. We will use R for our analyses, except for the second stage which will be performed in a Bayesian setting using R2Jags.DiscussionThis is a study protocol for developing a model to predict treatment response for RA patients receiving CTZ plus MTX in comparison with MTX alone, using a two-stage approach based on IPD-MA. The study will use a new modeling approach, which aims at retaining the statistical power. The model may help clinicians individualize treatment for particular patients.Systematic review registrationPROSPERO registration number pending (ID#157595).
Highlights
A model that can predict treatment response for a patient with specific baseline characteristics would help decision-making in personalized medicine
With regard to patient or trial characteristics to be used as potential covariates in the prognostic model, based on the literature [30,31,32] and our clinical practice, we propose the following factors as candidates of potential prognostic factors (PFs, baseline factors that may affect the response regardless of the treatment) (Table 1), which will be used for baseline risk model development
We have presented the study protocol for a prediction model of treatment effects for rheumatoid arthritis (RA) patients receiving CTZ plus MTX, using a two-stage approach based on Individual participant data (IPD)-MA
Summary
A model that can predict treatment response for a patient with specific baseline characteristics would help decision-making in personalized medicine. The aim of the study is to develop such a model in the treatment of rheumatoid arthritis (RA) patients who receive certolizumab (CTZ) plus methotrexate (MTX) therapy, using individual participant data meta-analysis (IPD-MA). According to the treat-to-target strategy proposed by the EULAR (European League Against Rheumatism) practice guideline [1], repeated assessment of disease activity should be performed every 3 to 6 months after a treatment is given, to evaluate the response and decide the next-step strategy: switching drugs, maintenance, tapering, or discontinuation. The disease course of RA is composed of many short-term (3 to 6 months) intervention-response loops. For the purpose of improving long-term prognosis, such as delaying the progression of bone fusion or functional deficiency, short-term intervention-response loops need to have beneficial outcomes [2]
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