Lifetime Data Anal - Joint modeling approach for semicompeting risks data with missing nonterminal event status.

Tópicos

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Resumo

Semicompeting risks data, where a subject may experience sequential non-terminal and terminal events, and the terminal event may censor the non-terminal event but not vice versa, are widely available in many biomedical studies. We consider the situation when a proportion of subjects' non-terminal events is missing, such that the observed data become a mixture of "true" semicompeting risks data and partially observed terminal event only data. An illness-death multistate model with proportional hazards assumptions is proposed to study the relationship between non-terminal and terminal events, and provide covariate-specific global and local association measures. Maximum likelihood estimation based on semiparametric regression analysis is used for statistical inference, and asymptotic properties of proposed estimators are studied using empirical process and martingale arguments. We illustrate the proposed method with simulation studies and data analysis of a follicular cell lymphoma study.

Resumo Limpo

semicompet risk data subject may experi sequenti nontermin termin event termin event may censor nontermin event vice versa wide avail mani biomed studi consid situat proport subject nontermin event miss observ data becom mixtur true semicompet risk data partial observ termin event data illnessdeath multist model proport hazard assumpt propos studi relationship nontermin termin event provid covariatespecif global local associ measur maximum likelihood estim base semiparametr regress analysi use statist infer asymptot properti propos estim studi use empir process martingal argument illustr propos method simul studi data analysi follicular cell lymphoma studi

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