中国科学院林存洁教授“偏置数据和治疗参数估计的半参数模型”

 

　　Semiparametric model for right-censored length-biased data andsemiparametric estimation of treatment effects

 

　　Analysis of right-censored and length-biased data is commonly encountered in prevalent cohort studies. The special structure of length-biased data is different from the structure of traditional survival data and the methods for traditional survival data cannot be directly applied to length-biased data since the independent censoring assumption is often violated in the presence of biased sampling. We propose the varying-coefficient transformation model and semiparametric varying-coefficient model analyzing the covariate effects on the population survival time under length-biased sampling. On the other hand, treatment effect is an important index in comparing two-sample data in survival analysis, industry manufacture, clinical medicine and many other applications. We propose a unified semiparametric approach to estimate different types of treatment effects under a case-control sampling plan with the logistic regression model assumption. Here, we allow that the estimating functions are not smooth with respect to parameters. We prove that the estimator based on the estimating equation is consistent and asymptotically normal and the empirical log-likelihood ratio statistic has a limiting scaled chi-square distribution.