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Title: Recursive partitioning based approaches for low back pain subgroup identification in individual patient data meta-analyses
Author: Mistry, Dipesh
ISNI:       0000 0004 5363 8112
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2014
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This thesis presents two novel approaches for performing subgroup analyses or identifying subgroups in an individual patient data (IPD) meta-analyses setting. The work contained in this thesis originated from an important research priority in the area of low back pain (LBP); identifying subgroups that most (or least) benefit from treatment. Typically, a subgroup is evaluated by applying a statistical test for interaction between a baseline characteristic and treatment. A systematic review found that subgroup analyses in the area of LBP are severely underpowered and are of a rather poor quality (Chapter 4). IPD meta-analyses provide an ideal framework with improved statistical power to investigate and identify 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 subgroups are typically investigated one at a time. As individuals have multiple characteristics that may be related to response to treatment, alternative statistical methods are required to overcome the associated issues. Tree based methods are a promising alternative that systematically search the entire covariate space to identify subgroups defined by multiple characteristics. In this work, a number of relevant tree methods, namely the Interaction Tree (IT), Simultaneous Threshold Interaction Modelling Algorithm (STIMA) and Subpopulation Identification based on a Differential Effect Search (SIDES), were identified and evaluated in a single trial setting in a simulation study. The most promising methods (IT and SIDES) were extended for application in an IPD meta-analyses setting by incorporating fixed-effect and mixed-effect models to account for the within trial clustering in the hierarchical data structure, and again assessed in a simulation study. Thus, this work proposes two statistical approaches to subgroup analyses or subgroup identification in an IPD meta-analysis framework. Though the application is based in a LBP setting, the extensions are applicable in any research discipline where subgroup analyses in an IPD meta-analysis setting is of interest.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: R Medicine (General)