STATISTICAL SEMI-PARAMETRIC METHODS FOR CANCER STUDIES

我们建议调查以下三个重要问题 癌症研究中出现的统计问题。 结果从-这 研究在分析癌症研究中会非常有用。 这些 问题是：1）线性回归模型的半参数方法 数据被删失。 我们将研究有效的数值算法， 实现Wei，Ying，和 Lin（1990）使用删失数据;研究 大样本理论及其实际样本量计算方法; 将他们的方法推广到多变量失效时间数据;研究 监测癌症生存试验的程序;探索线性 高度分层失效时间观测的回归方法;以及 研究巴克利-詹姆斯手术2)回归的失拟检验 模型 我们将研究Su和Wei（1990）提出的失拟检验 对于相关数据;检查他们的方法， 分类数据;并研究线性回归诊断方法， 删失数据3)重复顺序分类响应数据的分析。 我们将在这样的数据结构下研究混合效应模型。

We propose to investigate the following three important statistical problems arising in cancer studies. The results from-this research would be quite useful in analyzing cancer studies. These problems are: 1) Semi-parametric methods for the linear regression model with censored data. We will study efficient numerical algorithms to implement the rank linear regression methods proposed by Wei, Ying, and Lin (1990) with censored data; investigate the appropriateness of the large sample theory for their procedures with practical sample sizes; generalize their methods to the multivariate failure time data; study procedures for monitoring cancer survival trials; explore linear regression methods for highly stratified failure time observations; and study the Buckley-James procedures. 2) Lack-of-fit test for regression models. We will study the lack-of-fit test proposed by Su and Wei (1990) for the correlated data; examine their methods for cases with ordinal categorical data; and study linear regression diagnostic methods with censored data. 3) Analysis of repeated order categorical response data. We will study the mixed effects model under such a data structure.