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In biomedical and public health association studies, binary outcome variablesmay be subject to misclassification, resulting in substantial bias in effectestimates. The feasibility of addressing binary outcome misclassification inregression models is often hindered by model identifiability issues. In thispaper, we characterize the identifiability problems in this class of models asa specific case of "label switching" and leverage a pattern in the resultingparameter estimates to solve the permutation invariance of the complete datalog-likelihood. Our proposed algorithm in binary outcome misclassificationmodels does not require gold standard labels and relies only on the assumptionthat outcomes are correctly classified at least 50% of the time. A labelswitching correction is applied within estimation methods to recover unbiasedeffect estimates and to estimate misclassification rates. Open source softwareis provided to implement the proposed methods. We give a detailed simulationstudy for our proposed methodology and apply these methods to data from the2020 Medical Expenditure Panel Survey (MEPS).

Statistical inference for association studies in the presence of binary outcome misclassification