Abstract
In many clinical studies, there are two dependent event times with one of the events being terminal, such as death, and the other being nonfatal, such as myocardial infarction or cancer relapse. Morbidity can be dependently censored by mortality, but not vice versa. Asymptotic theory is developed for simultaneous estimation of the marginal distribution functions in this semi-competing risks setting. We specify the joint distribution of the event times in the upper wedge, where the nonfatal event happens before the terminal event, with the popular gamma frailty model. The estimators are based on an adaptation of the self-consistency principle. To study their properties, we employ a modification of the functional delta-method applied to Z-estimators. This approach to weak convergence leads naturally to asymptotic validity of both the nonparametric and multiplier bootstraps, facilitating inference in spite of the complexity of the limiting distribution.
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Kosorok, M.R., Fine, J.P., Jiang, H. et al. Asymptotic Theory for the Gamma Frailty Model with Dependent Censoring. Annals of the Institute of Statistical Mathematics 54, 476–499 (2002). https://doi.org/10.1023/A:1022450708498
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DOI: https://doi.org/10.1023/A:1022450708498