Key result
New EM algorithm incorporates serial measurements and misspecified binary outcomes in AF data.
Why the study?
Binary disease outcomes can be measured incorrectly, leading to biased estimators when modeling disease status related to multiple serial measurements.
This methodological study proposes a statistical approach to correct for bias when binary dependent variables are misspecified in longitudinal logistic models, illustrating its utility with atrial fibrillation recurrence data.
May correct bias in longitudinal AF models with misspecified outcomes; extends statistical tools but requires validation before use.
Many medical applications are interested to know the disease status. The disease status can be related to multiple serial measurements. Nevertheless, owing to various reasons, the binary outcome can be measured incorrectly. The estimators derived from the misspecified outcome can be biased. This paper derives the complete data likelihood function to incorporate both the multiple serial measurements and the misspecified outcome. Owing to the latent variables, EM algorithm is used to derive the maximum-likelihood estimators. Monte Carlo simulations are conducted to compare the impact of misspecification on the estimates. A retrospective data for the recurrence of atrial fibrillation is used to illustrate the usage of the proposed model.
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Wang et al. (2021) studied Atrial fibrillation. Complete data likelihood function with EM algorithm was evaluated on Impact of misspecification on estimates. A complete data likelihood function using the EM algorithm was developed to incorporate multiple serial measurements and misspecified binary outcomes, illustrated using atrial fibrillation data.
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