Misspecification of a binary dependent variable in the logistic model controlling for the repeated longitudinal measures. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Wang, Chun-Chao; Hwang, Yi-Ting; Chou, Chung-Chuan; Lee, Hui-Ling
• Is Part Of: Journal of applied statistics. Volume 50:Issue 1(2023)
• Page Start: 155
• Page End: 169
• Publication Date: 2023-01-02
• Subjects: Atrial fibrillation -- EM algorithm -- joint likelihood function -- logistic regression -- misspecification -- random effect model; 62P10; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2021.1982877
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : 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.