Methods for the Analysis of Longitudinal Data

描述(由申请者提供)：纵向研究在医学和公共卫生的所有领域都扮演着重要的角色。它们在公共卫生中对于了解慢性病的发展，以及研究改变疾病发展过程的因素都是重要的。此外，关于疾病进展和生活质量标记物的纵向数据是癌症和艾滋病治疗临床试验的一个日益重要的组成部分。 在健康科学的纵向研究中，缺失数据是规则，而不是例外。纵向研究经常出现辍学和/或间歇性无反应。该项目的主要目标是解决当无答复取决于原则上应该获得的具体值时，即当无答复不可忽视时所产生的统计问题。许多拟议的工作是基于一类纵向结果和非响应指标的联合模型，称为模式混合模型。模式混合模型具有许多重要的优点，例如，它们可以很容易地实现，并且可以直接表征模型假设。然而，模式混合模型有一个非常重要的缺点，到目前为止，它在许多应用领域中的实用性受到了限制：通常科学上感兴趣的自然回归参数是不可用的。 为了克服模式混合模式的这一公认缺点，具体目标1-3的重点是开发和实施处理不可忽视的辍学和间歇性缺失的新方法，方法是对给定无反应模式的重复措施的条件分布采用“边缘化”参数。具体目标4侧重于开发不可忽略的无响应的模式混合模型，当它依赖于外部协变量而不是直接依赖于未观察到的响应时。最后，具体目标5侧重于开发近似但计算简单的方法，用于在重复的二进制数据分析中处理可忽略的无响应。将要开发的统计方法将应用于精神分裂症和避孕疗法的临床试验数据，以及来自儿童肥胖症纵向流行病学研究的数据；然而，在通常出现纵向数据的更广泛的应用中，这些方法将同样有用。

DESCRIPTION (provided by applicant): Longitudinal studies play a prominent role in all areas of medicine and public health. They are important in public health in understanding the development of chronic illness, and in studying the factors that alter the course of disease development. In addition, longitudinal data on markers of diseases progression and quality of life are an increasingly important component of clinical trials of therapies for cancer and AIDS. In longitudinal studies in the health sciences, missing data are the rule, not the exception. Longitudinal studies frequently suffer from dropouts and/or intermittent non-response. The primary goal of this project is to address the statistical problems that arise when non-response depends on the specific value that in principle should have been obtained, i.e., when non-response is non-ignorable. Much of the proposed work is based on a class of joint models for the longitudinal outcomes and the non-response indicators known as pattern mixture models. Pattern-mixture models offer many important advantages, e.g., they can be easily implemented and it is straightforward to characterize model assumptions. However, pattern-mixture models have one very important drawback that has, so far, limited their usefulness in many areas of application: the natural regression parameters of usual scientific interest are not available. With the goal of overcoming this acknowledged shortcoming of pattern-mixture models, the focus of Specific Aims 1-3 is on developing and implementing new methods for handling non-ignorable dropout and intermittent missingness by adopting "marginalized" parameterizations for the conditional distribution of the repeated measures given non-response patterns. Specific Aim 4 focuses on developing pattern-mixture models for non-ignorable non-response when it depends on extraneous covariates rather than directly upon unobserved responses. Finally, Specific Aim 5 focuses on developing approximate, but computationally simple, methods for handling ignorable non-response in the analysis of repeated binary data. The statistical methods to be developed will be applied to clinical trial data for schizophrenia and contraceptive therapies, and to data from a longitudinal epidemiological study of obesity in children; however, the methods will be equally useful in a much broader range of applications where longitudinal data commonly arise.