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Dynamic semiparametric transformation models for recurrent event data with a terminal event

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Recurrent event data with a terminal event commonly arise in many longitudinal follow‐up studies. This article proposes a class of dynamic semiparametric transformation models for the marginal mean functions of… Click to show full abstract

Recurrent event data with a terminal event commonly arise in many longitudinal follow‐up studies. This article proposes a class of dynamic semiparametric transformation models for the marginal mean functions of the recurrent events with a terminal event, where some covariate effects may be time‐varying. An estimation procedure is developed for the model parameters, and the asymptotic properties of the resulting estimators are established. In addition, relevant significance tests are suggested for examining whether or not covariate effects vary with time, and a model checking procedure is presented for assessing the adequacy of the proposed models. The finite sample performance of the proposed estimators is examined through simulation studies, and an application to a medical cost study of chronic heart failure patients is provided.

Keywords: event data; data terminal; event; terminal event; recurrent event; dynamic semiparametric

Journal Title: Statistics in Medicine
Year Published: 2022

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