Methods for the Analysis of Longitudinal Data

描述（由申请人提供）：纵向研究在医学和公共卫生的所有领域发挥着突出的作用。在公共卫生方面，它们对了解慢性病的发展和研究改变疾病发展过程的因素很重要。此外，关于疾病进展和生活质量标志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，数据缺失是规律，而不是例外。纵向研究经常遭受脱落和/或间歇性无反应。该项目的主要目标是解决当不答复取决于原则上应该获得的具体值时出现的统计问题，即，当无反应是不可否认的。大部分拟议的工作是基于一类联合模型的纵向成果和非反应指标称为模式混合模型。模式混合模型提供了许多重要的优点，例如，它们可以很容易地实现，并且可以直接描述模型假设的特征。然而，模式混合模型有一个非常重要的缺点，到目前为止，限制了它们在许多应用领域的有用性：通常科学感兴趣的自然回归参数不可用。 为了克服模式混合模型的这一公认缺点，具体目标1-3的重点是开发和实施新的方法，通过采用“边缘化”参数化的条件分布的重复测量给定的无响应模式，处理不可重复的辍学和间歇性失踪。具体目标4的重点是开发模式混合模型的非线性无响应时，它取决于外来的协变量，而不是直接在未观察到的反应。最后，具体目标5侧重于开发近似的，但计算简单的方法，用于处理重复二进制数据分析中的可重复无响应。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，以及儿童肥胖症纵向流行病学研究的数据;然而，这些方法在纵向数据通常出现的更广泛的应用中同样有用。

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.