In longitudinal follow-up studies, recurrent event data with a terminal event are frequently encountered. In certain situations, some subjects drop out of the study for a period of time for various reasons before returning to the study again and this may happen more than once. Disregarding these gaps and treating the data as a regular recurrent event dataset may lead to biased estimations and potentially misleading conclusions. In this paper, we propose a flexible additive-multiplicative rates model for the analysis of recurrent event data with a terminal event and multiple intermittent gaps. This model allows for both additive and multiplicative effects of covariates, while leaving the dependence structure among the recurrent and terminal events unspecified. To infer the parameters of interest in the proposed model, we construct unbiased estimating equations and establish the asymptotic properties of the resulting estimators. Simulation studies demonstrate that considering intermittent gaps yields more accurate estimations in finite samples compared to the naive method. An application to a medical cost study of chronic heart failure is provided for illustration.
Cui et al. (Thu,) studied this question.