STATISTICAL METHODS FOR RANDOMLY REPEATED MEASURES

描述：（改编自研究者摘要）在临床试验和 在纵向研究中，结果测量通常包括：（1） 事件数量和这些事件严重程度的多重响应： 或（2）事件发生的时间和多个响应的 与事件对应的严重性。 在这个提议中。 这种类型的数据 被称为随机重复的结果测量。 广泛的，长期的 该提案的目标是制定统计方法， 随机重复结果测量的数据。 例如，在国家 心肺血液研究所II型临床试验，结局指标 从第5年随访时进行的患者血管造影中获得 对于冠心病患者来说。 这些措施是数字 血管病变（事件数量）和阻塞百分比 每处病变（事件的严重程度）。 病变发生的时间 未知。 在疾病中心进行的一项纵向研究中， 控制和预防，结果措施是住院（时间 事件发生率）和住院时间（这些事件的严重程度 感染人类免疫缺陷病毒的围产期儿童 （艾滋病毒）。 这两个例子代表了随机重复结果的数据 分别在情况（1）和（2）中采取措施。 疾病状态的变化是 这往往反映在事件数量的同时变化， 这些事件的多个严重程度反应（病例（1）），或同时 事件发生时间的变化和多重严重性 这些事件的反应（病例（2））。 重要的是要有意义地建模 这两个结果响应在这种类型的数据，以执行 充分比较各种治疗方法和有效评估风险 因素 然而，这方面的研究非常有限。 的 在该授权中提出的方法包括基于可能性的模型，边缘或 随机效应模型、图形诊断和拟合优度检验 模型拟合 将分析医学研究中的五个不同数据集 以证明所提出的方法的有效性。 的结果 这项研究将为医学研究人员提供急需的新的 统计学方法，通过随机重复测量来分析数据。

DESCRIPTION: (Adapted from investigator's abstract) In clinical trials and longitudinal studies, the outcome measures can often consist of: (1) the number of events and the multiple responses of the severity of these events: or (2) the time occurrences of events and the multiple responses of the severity corresponding to the events. In this proposal. this type of data is termed as randomly repeated outcome measures. The broad, long term objectives of this proposal are to develop statistical methods for analyzing data with randomly repeated outcome measures. For example, in the National Heart, Lung and Blood Institute TYPE II clinical trial, the outcome measures are obtained from patient's angiograms performed at the fifth year follow-up for patients with coronary artery disease. These measures are the numbers of vascular lesions (number of events) and the percentage of blockage of each lesion (severity of an event). The time occurrences of the lesions were unknown. In a longitudinal study conducted by the Centers for Disease Control and Prevention, the outcome measures are hospital admissions (time occurrences of events) and lengths of hospital stays (severity of these events) for perinatally infected children with human immunodeficiency virus (HIV). These two examples represent the data with randomly repeated outcome measures in cases (1) and (2), respectively. A change in disease status is often reflected by simultaneous changes in the number of the events and the multiple severity responses of these events (case (1)), or by simultaneous changes in the time occurrences of the events and the multiple severity responses of these events (case (2)). It is important to meaningfully model both of the outcome responses in this type of data in order to perform adequate comparisons of various treatments and effective evaluations of risk factors. However, very limited research has been done in this area. The methods proposed in this grant include likelihood-based models., marginal or random effects model, graphical diagnostics and goodness-of-fit tests for model fitting. Five different data sets in medical studies will be analyzed to demonstrate the usefulness of the proposed methods. The results from this research will provide medical researchers the much needed new statistical methods to analyze data with randomly repeated measures.