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Exact conditional and unconditional sample size for pair-matched studies with binary outcome: a practical guide

P Royston1

  • 1Department of Medical Physics, Royal Postgraduate Medical School, London, U.K.

Statistics in Medicine
|April 15, 1993
PubMed
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This study presents sample size tables for pair-matched studies with binary outcomes using exact tests. It also offers an approximate method for sample size estimation and a strategy for handling nuisance parameters in clinical trials.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Epidemiology

Background:

  • Determining appropriate sample sizes is crucial for the statistical power of pair-matched studies.
  • Binary outcomes are common in clinical research, necessitating specific sample size calculation methods.
  • Existing methods may not cover all parameter values or account for nuisance parameters effectively.

Purpose of the Study:

  • To provide comprehensive tables for sample size calculations in pair-matched studies with binary outcomes.
  • To introduce an approximate procedure for estimating sample sizes when exact table entries are unavailable.
  • To recommend a practical strategy for sample size estimation in the presence of discordant pairs.

Main Methods:

  • Development of sample size tables based on conditional and unconditional exact (binomial) tests.

Related Experiment Videos

  • Proposal of an approximate sample size estimation procedure modifying the Connett et al. large-sample formula.
  • Formulation of a strategy for estimating overall sample size considering the proportion of discordant pairs.

    • Main Results:

    • Presented tables offer direct sample size values for various scenarios in pair-matched studies.
    • The approximate procedure provides accurate sample size estimates for non-tabulated parameter values.
    • The recommended strategy effectively addresses the impact of nuisance parameters on sample size.

    Conclusions:

    • The provided tables and methods facilitate accurate sample size determination for pair-matched binary outcome studies.
    • The approximate procedure enhances flexibility in sample size planning.
    • The strategy for nuisance parameters improves the robustness of sample size estimations in clinical trial design.