A recursive partitioning approach for subgroup identification in individual patient data meta-analysis

Background: Motivated by the setting of clinical trials in low back pain, this work investigated statistical methods to identify patient subgroups for which there is a large treatment effect (treatment by subgroup interaction). Statistical tests for interaction are often underpowered. Individual patient data (IPD) meta-analyses provide a framework with improved statistical power to investigate subgroups. However, conventional approaches to subgroup analyses applied in both a single trial setting and an IPD setting have a number of issues, one of them being that factors used to define subgroups are investigated one at a time. As individuals have multiple characteristics that may be related to response to treatment, alternative exploratory statistical methods are required. Methods: Tree-based methods are a promising alternative that systematically searches the covariate space to identify subgroups defined by multiple characteristics. A tree method in particular, SIDES, is described and extended for application in an IPD meta-analyses setting by incorporating fixed-effects and random-effects models to account for between-trial variation. The performance of the proposed extension was assessed using simulation studies. The proposed method was then applied to an IPD low back pain dataset. Results: The simulation studies found that the extended IPD-SIDES method performed well in detecting subgroups especially in the presence of large between-trial variation. The IPD-SIDES method identified subgroups with enhanced treatment effect when applied to the low back pain data. Conclusions: This work proposes an exploratory statistical approach for subgroup analyses applicable in any research discipline where subgroup analyses in an IPD meta-analysis setting are of interest.