Semiparametric regression analysis of panel binary data with an informative observation process

• Published in: Computational statistics Vol. 40; no. 3; pp. 1285 - 1309
• Main Authors: Ge, Lei, Li, Yang, Sun, Jianguo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2025; Springer Nature B.V
• Subjects: Algorithms; Binary data; Data analysis; Economic Theory/Quantitative Economics/Mathematical Methods; Generalized linear models; Mathematics and Statistics; Original Paper; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Regression analysis; Statistics; Latent variable; Proportional mean model; Health and Retirement Study; Panel binary data; EM algorithm
• ISSN: 0943-4062; 1613-9658
• DOI: 10.1007/s00180-024-01528-8

Panel binary data arise in an event history study when study subjects are observed only at discrete time points instead of continuously and the only available information on the occurrence of the recurrent event of interest is whether the event has occurred over two consecutive observation times or each observation window. Although some methods have been proposed for regression analysis of such data, all of them assume independent observation times or processes, which may not be true sometimes. To address this, we propose a joint modeling procedure that allows for informative observation processes. For the implementation of the proposed method, a computationally efficient EM algorithm is developed and the resulting estimators are consistent and asymptotically normal. The simulation study conducted to assess its performance indicates that it works well in practical situations, and the proposed approach is applied to the motivating data set from the Health and Retirement Study.