A class of semiparametric cure models with current status data

Guoqing Diao () and Ao Yuan
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Guoqing Diao: George Mason University
Ao Yuan: Georgetown University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2019, vol. 25, issue 1, No 2, 26-51

Abstract: Abstract Current status data occur in many biomedical studies where we only know whether the event of interest occurs before or after a particular time point. In practice, some subjects may never experience the event of interest, i.e., a certain fraction of the population is cured or is not susceptible to the event of interest. We consider a class of semiparametric transformation cure models for current status data with a survival fraction. This class includes both the proportional hazards and the proportional odds cure models as two special cases. We develop efficient likelihood-based estimation and inference procedures. We show that the maximum likelihood estimators for the regression coefficients are consistent, asymptotically normal, and asymptotically efficient. Simulation studies demonstrate that the proposed methods perform well in finite samples. For illustration, we provide an application of the models to a study on the calcification of the hydrogel intraocular lenses.

Keywords: Box–Cox transformation; Cure fraction; Empirical process; NPMLE; Proportional hazards cure model; Proportional odds cure model; Semiparametric efficiency (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10985-018-9420-0

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