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Sample size and power considerations in network meta-analysis.

Kristian Thorlund1, Edward J Mills

  • 1Department of Clinical Epidemiology and Biostatistics, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4 K1. thorluk@mcmaster.ca

Systematic Reviews
|September 21, 2012
PubMed
Summary
This summary is machine-generated.

New methods estimate effective sample size and statistical power in network meta-analysis. These tools help assess evidence strength for comparative effectiveness, aiding regulatory agencies and decision-makers.

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Area of Science:

  • Biostatistics
  • Evidence Synthesis

Background:

  • Network meta-analysis (NMA) is increasingly used for comparative effectiveness research.
  • NMA presents complex methodological challenges, particularly regarding sample size and statistical power.
  • Existing methods for evaluating sample size and power in NMA are lacking.

Purpose of the Study:

  • To develop practical methods for estimating effective sample size in indirect comparison and network meta-analysis.
  • To create approaches for retrospectively assessing statistical power within NMA.

Main Methods:

  • Developed flexible methods to estimate 'effective sample size' for treatment comparisons in NMA.
  • Proposed techniques for calculating statistical power for individual comparisons within an NMA framework.
  • Utilized a smoking cessation NMA dataset with over 100 trials for illustration.

Main Results:

  • Introduced user-friendly methods for quantifying effective sample size in NMA.
  • Enabled retrospective estimation of statistical power for each comparison in an NMA.
  • Demonstrated method performance using a real-world smoking cessation intervention dataset.

Conclusions:

  • The developed methods are easy to implement and valuable for assessing evidence strength in NMA.
  • These tools will assist regulatory agencies and decision-makers in evaluating comparative effectiveness claims.