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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Sample size evaluation for a multiply matched case-control study using the score test from a conditional logistic

John M Lachin1

  • 1Department of Epidemiology and Biostatistics, The Biostatistics Center, The George Washington University, Rockville, MD 20852, USA. jml@biostat.bsc.gwu.edu

Statistics in Medicine
|September 22, 2007
PubMed
Summary

Conditional logistic regression models, identical to stratified Cox models, assess covariate effects in matched studies. This research provides power equations for detecting covariate effects and determining sample sizes in nested and multiply matched case-control studies.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Conditional logistic regression (CLR) is a statistical method for analyzing matched case-control studies.
  • The CLR likelihood is equivalent to the stratified Cox proportional hazards model, accounting for ties.
  • This model is applicable to nested case-control and multiply matched case-control study designs.

Purpose of the Study:

  • To derive simple equations for statistical power in CLR models.
  • To determine the number of cases and controls needed for a desired power level.
  • To provide methods for assessing qualitative and quantitative covariate effects on risk.

Main Methods:

  • Utilized the score test distribution for covariate effects within the CLR model.
  • Derived equations for statistical power based on the coefficient (theta).
  • Developed expressions for quantitative covariates (mean differences) and qualitative covariates (exposure probabilities).

Main Results:

  • Simple equations were derived to describe the power of the score test.
  • Formulas were established to calculate the required number of cases and controls for desired power.
  • The method applies to both nested and multiply matched case-control studies.

Conclusions:

  • The derived equations offer a practical approach to sample size and power calculations in matched case-control studies.
  • This facilitates efficient study design and analysis for covariate effect assessment.
  • The findings are relevant for epidemiological research employing matched designs.