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Two-stage individual participant data meta-analysis and forest plots

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  • David Fisher

    (MRC Clinical Trials Unit Hub for Trials Methodology Research)

Abstract

Stata has a wide range of tools for performing meta-analysis, but presently not individual participant data (IPD) meta-analysis, in which the analysis units are within-study observations (for example, patients) rather than aggregate study results. I present ipdmetan, a command that facilitates two-stage IPD meta-analysis by fitting a specified model to the data of each study in turn and storing the results in a matrix. Features include subgroups, inclusion of aggregate (for example, published) data, iterative estimates and confidence limits for the tau-squared measure of heterogeneity, and the analysis of treatment-covariate interactions. This last is a great benefit of IPD collection and is a subject on which my colleagues and I have published previously (Fisher et al., 2011, Journal of Clinical Epidemiology 64: 949–967). I shall discuss how ipdmetan facilitates our recommended approach and its strengths and weaknesses, in particular one-stage versus two-stage modeling and within- and between-trial information. In addition, the graphics subroutine written for the metan package (Harris et al., 2008, Stata Journal 8: 3–28) has been greatly expanded to enable flexible, generalized forest plots for a variety of settings. I shall demonstrate some of the possibilities and encourage feedback on how this may be developed further. Examples will be given using real-world IPD meta-analyses of survival data in cancer, although the programs are applicable generally.

Suggested Citation

  • David Fisher, 2013. "Two-stage individual participant data meta-analysis and forest plots," United Kingdom Stata Users' Group Meetings 2013 19, Stata Users Group.
    • Handle: RePEc:boc:usug13:19
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