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J Clin Epidemiol. 2011 Sep;64(9):949-67. doi: 10.1016/j.jclinepi.2010.11.016. Epub 2011 Mar 16.

A critical review of methods for the assessment of patient-level interactions in individual participant data meta-analysis of randomized trials, and guidance for practitioners.

Author information

1
Medical Research Council Clinical Trials Unit, London NW1 2DA, UK. df@ctu.mrc.ac.uk

Abstract

OBJECTIVE:

Treatments may be more effective in some patients than others, and individual participant data (IPD) meta-analysis of randomized trials provides perhaps the best method of investigating treatment-covariate interactions. Various methods are used; we provide a comprehensive critique and develop guidance on method selection.

STUDY DESIGN AND SETTING:

We searched MEDLINE to identify all frequentist methods and appraised them for simplicity, risk of bias, and power. IPD data sets were reanalyzed.

RESULTS:

Four methodological categories were identified: PWT: pooling of within-trial covariate interactions; OSM: "one-stage" model with a treatment-covariate interaction term; TDCS: testing for difference between covariate subgroups in their pooled treatment effects; and CWA: combining PWT with meta-regression. Distinguishing across- and within-trial information is important, as the former may be subject to ecological bias. A strategy is proposed for method selection in different circumstances; PWT or CWA are natural first steps. The OSM method allows for more complex analyses; TDCS should be avoided. Our reanalysis shows that different methods can lead to substantively different findings.

CONCLUSION:

The choice of method for investigating interactions in IPD meta-analysis is driven mainly by whether across-trial information is considered for inclusion, a decision, which depends on balancing possible improvement in power with an increased risk of bias.

PMID:
21411280
DOI:
10.1016/j.jclinepi.2010.11.016
[Indexed for MEDLINE]

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