title: Bayesian approach for flexible modeling of semicompeting risks data.
abstract: Semicompeting risks data arise when two types of events, non-terminal and terminal, are observed. When the terminal event occurs first, it censors the non-terminal event, but not vice versa. To account for possible dependent censoring of the non-terminal event by the terminal event and to improve prediction of the terminal event using the non-terminal event information, it is crucial to model their association properly. Motivated by a breast cancer clinical trial data analysis, we extend the well-known illness-death models to allow flexible random effects to capture heterogeneous association structures in the data. Our extension also represents a generalization of the popular shared frailty models that usually assume that the non-terminal event does not affect the hazards of the terminal event beyond a frailty term. We propose a unified Bayesian modeling approach that can utilize existing software packages for both model fitting and individual-specific event prediction. The approach is demonstrated via both simulation studies and a breast cancer data set analysis.
authors.affiliation: Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, 46285, U.S.A.
authors.forename: Baoguang
authors.initials: B
authors.lastname: Han
authors.forename: Menggang
authors.initials: M
authors.lastname: Yu
authors.forename: James J
authors.initials: JJ
authors.lastname: Dignam
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Rathouz
mesh_terms: D018931:Antineoplastic Agents, Hormonal / Q000008:administration & dosage / Q000494:pharmacology; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000188:drug therapy / Q000401:mortality* / Q000473:pathology; D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D003198:Computer Simulation; D018450:Disease Progression; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D009010:Monte Carlo Method; D009362:Neoplasm Metastasis; D011379:Prognosis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data; D016019:Survival Analysis; D013629:Tamoxifen / Q000008:administration & dosage / Q000494:pharmacology; D013997:Time Factors
chemical_list: D018931:Antineoplastic Agents, Hormonal; D013629:Tamoxifen
keywords: Markov chain Monte Carlo; illness-death; random effects; semicompeting risks