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Sample size and optimal design for logistic regression with binary interaction.

Eugene Demidenko1

  • 1Dartmouth Medical School, Hanover, NH 03755, U.S.A. eugened@dartmouth.edu

Statistics in Medicine
|July 20, 2007
PubMed
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This study recommends the Wald test for sample size and power calculations in logistic regression, correcting common errors. It provides new formulas for interaction studies, with an online calculator available.

Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Lack of consensus exists regarding the appropriate statistical test for sample size determination and power analysis in logistic regression.
  • Previous work established correct sample size formulas for logistic regression with single exposure.

Purpose of the Study:

  • To advocate for the use of the Wald test in sample size and power calculations.
  • To derive closed-form formulas for sample size and power in logistic regression with binary exposure and covariate, specifically for interaction studies.
  • To derive the optimal control-case ratio to maximize the power function.

Main Methods:

  • The study argues for the Wald test based on its common application in regression coefficient significance testing.
  • It corrects a prevalent error in sample size determination involving the null value estimation of the maximum likelihood estimate (MLE) variance.

Related Experiment Videos

  • Closed-form formulas for sample size and power in interaction studies are derived.

    • Main Results:

    • The Wald test is proposed as the appropriate statistic for power functions in logistic regression.
    • Corrected sample size determination methods are presented, addressing a widespread mistake.
    • New formulas for sample size and power in interaction studies with binary exposure and covariate are derived.
    • An optimal control-case ratio formula is provided.

    Conclusions:

    • The Wald test should be consistently used for sample size and power calculations in logistic regression.
    • The derived formulas offer accurate methods for sample size and power estimation in interaction studies.
    • An online tool is available for performing these calculations, facilitating practical application.