Full-likelihood approaches to misclassification of a binary exposure in matched case-control studies

Area of Science:

• Epidemiology
• Biostatistics

Background:

• Matched case-control studies are crucial for etiological research.
• Exposure misclassification is a common challenge in these studies.
• Varying matching ratios can complicate analysis.

Purpose of the Study:

• To develop a statistical model for analyzing matched case-control studies with binary exposure misclassification.
• To provide accurate estimates and confidence intervals for the odds ratio.
• To accommodate diverse matching ratios.

Main Methods:

• Extension of the conditional model using random effects or Bayesian formulations.
• Development of analytic forms for misclassified data.
• Simulation studies to evaluate model performance.

Main Results:

• The proposed model yields estimates and confidence intervals that converge to standard forms with zero error probability.
• Simulation studies with mixed matching ratios showed good interval coverage.
• Point estimates on the log scale exhibited slight bias.

Conclusions:

• The developed statistical approach effectively handles exposure misclassification in matched case-control studies.
• The model offers a robust framework for etiological research with potential for incorporating uncertainty in misclassification rates.