A semiparametric regression cure model with current status data

• 成果类型: Article
• 署名作者: Lam, KF; Xue, HQ
• 署名单位: University of Hong Kong; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/92.3.573
• 发表日期: 2005
• 页码: 573586
• 关键词: additive hazards regression; Mixture Model; efficient estimation; linear-regression

摘要

This paper considers the analysis of current status data with a cured proportion in the population using a mixture model that combines a logistic regression formulation for the probability of cure with a semiparametric regression model for the time to occurrence of the event. The semiparametric regression model belongs to the flexible class of partly linear models that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estimator for the unknown smooth function is obtained and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies were carried out to investigate the performance of the proposed method and the model is fitted to a dataset from a study of calcification of the hydrogel intraocular lenses, a complication of cataract treatment.