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Trial sequential methods for meta-analysis.

Elena Kulinskaya1, John Wood2

  • 1School of Computing Sciences, University of East Anglia, Norwich, U.K.

Research Synthesis Methods
|June 9, 2015
PubMed
Summary
This summary is machine-generated.

Sequential meta-analysis methods aid new trial design. New formulas address random effects models, determining minimum trials and sample sizes for efficient research, unlike previous fixed-effect approaches.

Keywords:
group sequential methodsheterogeneityinformation sizerandom effects modeltrial sequential analysis

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Evidence Synthesis

Background:

  • Sequential meta-analysis methods are applicable to designing new clinical trials.
  • Current methods, based on group sequential designs, face challenges within random effects models.
  • Trial sequential analysis (TSA) is an existing approach for sequential meta-analysis in trial design.

Purpose of the Study:

  • To extend sequential meta-analysis methods for designing new trials within a random effects framework.
  • To provide explicit formulae for determining the minimum number of trials and their sample sizes.
  • To address conceptual difficulties in applying sequential meta-analysis to random effects models.

Main Methods:

  • Developing statistical formulae for trial sequential analysis under random effects models.
  • Calculating requisite minimum number of trials and optimal sample sizes.
  • Illustrating the methodology with practical examples, including a meta-analysis of magnesium for myocardial infarction.

Main Results:

  • Existing TSA methods require adjustments for random effects models, particularly concerning heterogeneity.
  • The proposed formulae provide a framework for determining the minimum number of trials and their sample sizes.
  • Planning a larger number of smaller trials can potentially reduce the total number of patients needed.

Conclusions:

  • The developed formulae offer a more robust approach to trial sequential analysis for random effects meta-analysis.
  • Heterogeneity significantly influences the design, potentially necessitating multiple smaller trials for optimal patient allocation.
  • The findings have implications for efficient and effective clinical trial design in evidence synthesis.