Combining Aggregate Data and Individual Patient Data in Model‐Based Meta‐Analysis: An Illustrative Case Study of Tofacitinib in Rheumatoid Arthritis Patients

Model-based meta-analysis (MBMA) utilizes aggregate data (AD) and allows integration of information from multiple studies, which may provide more statistical power to detect clinically relevant treatment effects than an individual randomized controlled trial alone. Access to individual patient data (IPD) is often limited due to confidentiality; therefore, obtaining IPD associated with published literature data is challenging. Thus, to probe predictive covariates, one must rely on an adequate range of aggregate covariate data, or published stratified results could also be used. With access to IPD, or with access to published stratified results, estimates for predictive covariates could be improved. This work is primarily centered on quantifying the potential benefits of having access to IPD when performing MBMA. This was assessed using a 3-step approach. Two scenarios were explored: one to compare MBMAs with and without access to IPD, assuming no predictive covariates; and another to compare MBMAs with and without access to IPD, where a specific predictive covariate was known to be influential and was used to stratify IPD accordingly. The performance of the method was evaluated for different ratios of IPD studies versus AD studies. In the scenario where an MBMA with covariate was used, instead, the performance of the method was evaluated for different ratios of covariate stratified AD studies versus AD studies. Overall, the benefit of IPD over AD was not evident in the model without covariates, whereas including stratified IPD led to improved covariate model performance.