METHODS FOR ANALYSIS OF LONGITUDINAL AND FAMILIAL DATA

本项目将制定统计方法， 连续收集的至事件时间和重复测量数据 纵向研究。这些数据在许多环境中越来越普遍 在医学和公共卫生领域，包括旨在 评价姑息、维持或预防治疗， 在预防干预措施的影响的人口研究， 疾病发展的危险因素。我们的方法将是 应用于艾滋病、精神分裂症和避孕的临床试验数据 治疗，并对肥胖症的纵向流行病学研究， 童年. 该项目有两个主要组成部分， 重复测量和事件发生时间数据的联合分析。第一 组件将侧重于主要兴趣在于描述 在存在不可预测的情况下，分类结局随时间的趋势 辍学生这在临床试验中很常见， 可能与关注的结局相关，并导致随访停止。 这项工作的一个新特点是使用混合模型， 列入辍学情况。混合模型提供了两个重要的 优点：它们可以很容易地实现， 描述模型假设。我们将开发基于 广义估计方程（GEE）的情况下，任何中间 在dropout之前的缺失是完全随机缺失，并扩展 该方法可以对任意模式的非线性无响应进行删除。 我们还将开发最大似然方法，同时使用边际和 马尔可夫模型的情况下，中间缺失是失踪， 随机 第二，我们将重点关注的情况下，首要利益是生存 并且其中重复的测量被用于提高 审查的存在。该组件将扩展以前在此方面的工作， 问题通过使用分类重复测量的混合模型，通过 包括比例风险生存模型的协变量 假设，并通过使用最大似然估计联合 生存率和重复测量的分布。

This project will develop statistical methods for the joint analysis of time-to-event and repeated measures data collected serially in longitudinal studies. Such data are increasingly common in many settings in medicine and public health, including clinical trials designed to evaluate palliative, maintenance, or preventative therapies, in evaluation of prevention interventions, and in population studies of the effects of risk factors on the development of disease. Our methodology will be applied to clinical trial data for AIDS, schizophrenia, and contraception therapies, and to a longitudinal epidemiological study of obesity in childhood. There are two main components to the project, both of them involving the joint analysis of repeated measures and time-to-event data. The first component will focus on the case where primary interest is in describing trends in a categorical outcome over time in the presence of non-ignorable dropout. This is common in clinical trials where removal from treatment may be related to the outcome of interest and causes follow-up to cease. A new feature of this work will be the use of a mixture model to incorporate information on dropout. Mixture models offer two important advantages: they can be easily implemented and it is straightforward to characterize model assumptions. We will develop methods based on Generalized Estimating Equations (GEE) for the case where any intermediate missingness prior to dropout is Missing Completely at Random, and extend the method to del with arbitrary patterns of non-ignorable non-response. We will also develop maximum likelihood approaches using both marginal and Markov models for the case where intermediate missingness is Missing at Random. Second, we will focus on the case where the primary interest is survival and where the repeated measures are used to gain efficiency in the presence of censoring. This component will extend previous work on this problem by using mixture models for categorical repeated measures, by including covariates for the survival model with the proportional hazards assumption, and by using maximum likelihood to estimate the joint distribution of survival and repeated measures.