Abstract
In health and clinical research, panel binary data from recurrent events arise when subjects are surveyed to report occurrence statuses of recurrent events over fixed observation windows. In practice, such data can be cut short by a dependent failure event such as death. For the analysis of panel binary data, tools from generalized linear models overlook the recurrence nature of panel binary data, and other relevant literature does not accommodate the failure time. Motivated by the hospitalization data surveyed from the Health and Retirement Study, we propose a semiparametric joint-modeling-based procedure for analyzing panel binary data with a dependent failure time. For model fitting, we develop a computationally efficient EM algorithm and show the resulting estimates are consistent and asymptotically normal. Theoretical results are provided to enable valid inferences. Simulation studies have confirmed the performance of the proposed method in practical settings. The method is applied to assess important risk factors associated with incidences of hospitalization among the working elderly.