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A recursive partitioning approach for subgroup identification in individual patient data meta-analysis
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Mistry, Dipesh, Stallard, Nigel and Underwood, Martin (2018) A recursive partitioning approach for subgroup identification in individual patient data meta-analysis. Statistics in Medicine, 37 (9). pp. 1550-1561. doi:10.1002/sim.7609
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Official URL: http://doi.org/10.1002/sim.7609
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.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | R Medicine > RA Public aspects of medicine | ||||||||||||
| Divisions: | Faculty of Medicine > Warwick Medical School > Health Sciences > Population, Evidence & Technologies (PET) Faculty of Medicine > Warwick Medical School |
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| Library of Congress Subject Headings (LCSH): | Medical statistics, Recursive partitioning, Clinical trials, Backache -- Treatment, Decision trees | ||||||||||||
| Journal or Publication Title: | Statistics in Medicine | ||||||||||||
| Publisher: | John Wiley & Sons Ltd. | ||||||||||||
| ISSN: | 0277-6715 | ||||||||||||
| Official Date: | 30 April 2018 | ||||||||||||
| Dates: |
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| Date of first compliant deposit: | 28 November 2017 | ||||||||||||
| Volume: | 37 | ||||||||||||
| Number: | 9 | ||||||||||||
| Page Range: | pp. 1550-1561 | ||||||||||||
| DOI: | 10.1002/sim.7609 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Open Access | ||||||||||||
| RIOXX Funder/Project Grant: |
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