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”右审查和长度偏置数据的分析是普遍存在队列研究经常遇到。长度偏置数据的特殊结构不同于传统的存活数据和方法为传统的存活数据的结构不能被直接应用到长度偏置数据自独立结尾假设经常使用偏差抽样的存在。我们提出了不同的系数转换模型和半参数变系数模型分析的长度偏置抽样下的人口生存时间的协变量的影响。在另一方面，治疗是在生存分析，产业制造，临床医学和许多其他应用比较两个样本数据的一个重要指标。我们提出了一个单向网络版半参数的方法来估算下与逻辑回归模型的假设情况下，控制抽样方案的治疗类型。在这里，我们允许估算功能不顺畅方面的参数。我们证明，基于估计方程的估计是一致的，渐近正常和经验对数似然比统计有限制缩放卡方分布。以下是原文。

　　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.