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Statistical Analysis of Individual Participant Data Meta-Analyses: A Comparison of Methods and Recommendations for Practice

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  • Gavin B Stewart
  • Douglas G Altman
  • Lisa M Askie
  • Lelia Duley
  • Mark C Simmonds
  • Lesley A Stewart

Abstract

Background: Individual participant data (IPD) meta-analyses that obtain “raw” data from studies rather than summary data typically adopt a “two-stage” approach to analysis whereby IPD within trials generate summary measures, which are combined using standard meta-analytical methods. Recently, a range of “one-stage” approaches which combine all individual participant data in a single meta-analysis have been suggested as providing a more powerful and flexible approach. However, they are more complex to implement and require statistical support. This study uses a dataset to compare “two-stage” and “one-stage” models of varying complexity, to ascertain whether results obtained from the approaches differ in a clinically meaningful way. Methods and Findings: We included data from 24 randomised controlled trials, evaluating antiplatelet agents, for the prevention of pre-eclampsia in pregnancy. We performed two-stage and one-stage IPD meta-analyses to estimate overall treatment effect and to explore potential treatment interactions whereby particular types of women and their babies might benefit differentially from receiving antiplatelets. Two-stage and one-stage approaches gave similar results, showing a benefit of using anti-platelets (Relative risk 0.90, 95% CI 0.84 to 0.97). Neither approach suggested that any particular type of women benefited more or less from antiplatelets. There were no material differences in results between different types of one-stage model. Conclusions: For these data, two-stage and one-stage approaches to analysis produce similar results. Although one-stage models offer a flexible environment for exploring model structure and are useful where across study patterns relating to types of participant, intervention and outcome mask similar relationships within trials, the additional insights provided by their usage may not outweigh the costs of statistical support for routine application in syntheses of randomised controlled trials. Researchers considering undertaking an IPD meta-analysis should not necessarily be deterred by a perceived need for sophisticated statistical methods when combining information from large randomised trials.

Suggested Citation

  • Gavin B Stewart & Douglas G Altman & Lisa M Askie & Lelia Duley & Mark C Simmonds & Lesley A Stewart, 2012. "Statistical Analysis of Individual Participant Data Meta-Analyses: A Comparison of Methods and Recommendations for Practice," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-8, October.
  • Handle: RePEc:plo:pone00:0046042
    DOI: 10.1371/journal.pone.0046042
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    Cited by:

    1. Clark, Beth & Stewart, Gavin B. & Panzone, Luca A. & Kyriazakis, Ilias & Frewer, Lynn J., 2017. "Citizens, consumers and farm animal welfare: A meta-analysis of willingness-to-pay studies," Food Policy, Elsevier, vol. 68(C), pages 112-127.
    2. Mathur, Maya B & VanderWeele, Tyler, 2018. "Statistical methods for evidence synthesis," Thesis Commons kd6ja, Center for Open Science.
    3. Béranger Lueza & Audrey Mauguen & Jean-Pierre Pignon & Oliver Rivero-Arias & Julia Bonastre & MAR-LC Collaborative Group, 2016. "Difference in Restricted Mean Survival Time for Cost-Effectiveness Analysis Using Individual Patient Data Meta-Analysis: Evidence from a Case Study," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-12, March.
    4. Claudia Sala & Pietro Di Lena & Danielle Fernandes Durso & Andrea Prodi & Gastone Castellani & Christine Nardini, 2020. "Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-15, March.
    5. Maya B. Mathur & Tyler J. VanderWeele, 2020. "New statistical metrics for multisite replication projects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1145-1166, June.

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