Research Library

Premium A recursive partitioning approach for subgroup identification in individual patient data meta‐analysis
Author(s)
Mistry Dipesh,
Stallard Nigel,
Underwood Martin
Publication year2018
Publication title
statistics in medicine
Resource typeJournals
PublisherWiley
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.
Subject(s)biology , botany , clinical trial , computer science , covariate , data mining , exploratory data analysis , identification (biology) , machine learning , mathematics , medicine , meta analysis , missing data , multiple comparisons problem , random effects model , recursive partitioning , statistical power , statistics , subgroup analysis
Language(s)English
SCImago Journal Rank1.996
H-Index183
eISSN1097-0258
pISSN0277-6715
DOI10.1002/sim.7609

Seeing content that should not be on Zendy? Contact us.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here