Lifetime Data Anal - A class of semiparametric transformation models for survival data with a cured proportion.

Tópicos

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Resumo

We propose a new class of semiparametric regression models based on a multiplicative frailty assumption with a discrete frailty, which may account for cured subgroup in population. The cure model framework is then recast as a problem with a transformation model. The proposed models can explain a broad range of nonproportional hazards structures along with a cured proportion. An efficient and simple algorithm based on the martingale process is developed to locate the nonparametric maximum likelihood estimator. Unlike existing expectation-maximization based methods, our approach directly maximizes a nonparametric likelihood function, and the calculation of consistent variance estimates is immediate. The proposed method is useful for resolving identifiability features embedded in semiparametric cure models. Simulation studies are presented to demonstrate the finite sample properties of the proposed method. A case study of stage III soft-tissue sarcoma is given as an illustration.

Resumo Limpo

propos new class semiparametr regress model base multipl frailti assumpt discret frailti may account cure subgroup popul cure model framework recast problem transform model propos model can explain broad rang nonproport hazard structur along cure proport effici simpl algorithm base martingal process develop locat nonparametr maximum likelihood estim unlik exist expectationmaxim base method approach direct maxim nonparametr likelihood function calcul consist varianc estim immedi propos method use resolv identifi featur embed semiparametr cure model simul studi present demonstr finit sampl properti propos method case studi stage iii softtissu sarcoma given illustr

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