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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:632886 |
| Date | January 2014 |
| Creators | Mistry, Dipesh |
| Publisher | University of Warwick |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://wrap.warwick.ac.uk/64032/ |
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