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

Lei Ge (), Yang Li () and Jianguo Sun ()
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Lei Ge: Indiana University School of Medicine and Richard M. Fairbanks School of Public Health
Yang Li: Indiana University School of Medicine and Richard M. Fairbanks School of Public Health
Jianguo Sun: University of Missouri

Computational Statistics, 2025, vol. 40, issue 3, No 6, 1285-1309

Abstract: Abstract 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.

Keywords: EM algorithm; Health and Retirement Study; Latent variable; Panel binary data; Proportional mean model (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01528-8

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