METHODS FOR ANALYSIS OF LONGITUDINAL AND FAMILIAL DATA

该项目将开发统计方法，用于联合分析 连续收集的事件间隔时间和重复测量数据 纵向研究。这类数据在许多环境中越来越常见 在医学和公共卫生方面，包括旨在 评估姑息性、维持性或预防性治疗 预防干预措施，以及对艾滋病影响的人口研究 影响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，以及一项关于肥胖的纵向流行病学研究 童年。 该项目有两个主要组件，它们都涉及 对重复测量和事件发生时间数据的联合分析。第一 组件将主要关注的是描述 在存在不可忽视的情况下，随着时间的推移，绝对结果的趋势 辍学。这在临床试验中很常见，在临床试验中，从治疗中移除 可能与感兴趣的结果有关，并导致后续行动停止。 这项工作的一个新特点是使用混合模型来 纳入有关辍学的信息。混合模型提供了两个重要的 优点：它们可以很容易地实现，并且可以直接 描述模型假设的特征。我们将基于以下方面开发方法 任意中间变量情形的广义估计方程(GEE) 在RANDOM和EXTEND中，完全不存在辍学前的缺失 处理不可忽略的无响应的任意模式的方法。 我们还将开发最大似然方法，使用边际和 中间缺失情况下的马尔可夫模型 随机的。 第二，我们将重点关注以生存为首要利益的情况 如果重复的措施被用来在 审查的存在。该组件将扩展先前在此方面的工作 使用分类重复测量的混合模型的问题，通过 包含比例风险生存模型的协变量 假设，并通过使用最大似然来估计联合 生存分布和重复治疗。

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.