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

• Published in: Statistics in medicine Vol. 44; no. 5; pp. e10316 - n/a
• Main Authors: Hochstedler Webb, Kimberly A., Wells, Martin T.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 28.02.2025; Wiley Subscription Services, Inc
• Subjects: Algorithms; association studies; Bias; bias correction; Computer Simulation; Data Interpretation, Statistical; EM algorithm; Estimates; Humans; identification; label‐switching; Likelihood Functions; MCMC; Models, Statistical; Regression Analysis; Software; MCMC; identification; association studies; bias correction; EM algorithm; label‐switching

ABSTRACT In biomedical and public health association studies, binary outcome variables may be subject to misclassification, resulting in substantial bias in effect estimates. The feasibility of addressing binary outcome misclassification in regression models is often hindered by model identifiabilit...