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Statistical Inference for Association Studies in the Presence of Binary Outcome Misclassification

Kimberly A Hochstedler Webb1, Martin T Wells1

  • 1Department of Statistics and Data Science, Cornell University Ithaca, Ithaca, NY, USA.

Statistics in Medicine
|February 6, 2025
PubMed
Summary

This study introduces a new algorithm to correct for binary outcome misclassification bias in public health studies. The method corrects effect estimates without needing gold standard labels, improving accuracy in association studies.

Keywords:
EM algorithmMCMCassociation studiesbias correctionidentificationlabel‐switching

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