Work

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

Year: 2018

Type: article

Abstract: 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.

Source: Statistics in Medicine

DOI: 10.1002/sim.7609

Authors: Dipesh Mistry, Nigel Stallard, Martin Underwood

Institution: University of Warwick

Language: English

Topic: Statistical Methods in Clinical Trials

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $5,450