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Activity Number: 334 - Health Policy Statistics Student Paper Awards
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Health Policy Statistics Section
Abstract #302878
Title: A Latent Class Based Joint Model for Recurrence and Termination with Application to Heart Transplants
Author(s): Zhixing Xu* and Debajyoti Sinha and Jonathan R. Bradley
Companies: Florida State University and FLORIDA STATE UNIVERSITY and Florida State University
Keywords: Bayesian analysis; Frailty; Joint Model; Intensity and rate; Recurrent events

In many clinical studies, each patient is at risk of recurrent events as well as the terminating event. We present a novel latent-class based semiparametric joint model that offers clinically meaningful and estimable association between the recurrence profile and risk of termination. Unlike previous shared-frailty based models, this model has coherent interpretation of the covariate effects on all relevant functions and model quantities that are either conditional or unconditional on events history. We offer a fully Bayesian method for estimation and prediction using a complete specification of the prior process of the baseline functions. When there is a lack of prior information about the baseline functions, we derive a practical and theoretically justifiable partial likelihood based semiparametric Bayesian approach. Practical advantages of our methods are illustrated via a simulation study and the analysis of a transplant study with recurrent Non-Fatal Graft Rejections (NFGR) and the termination event of death due to total graft rejection. This analysis can have an important impact on health policy and our analysis shows important discrepancies among different racial groups.

Authors who are presenting talks have a * after their name.

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