HIERARCHICAL MODELING OF AIDS CLINICAL TRIALS DATA

描述(改编自摘要)：分层模型使 结合来自相似和独立实验的信息，产生 改进了对个人和共享模型特征的估计。 来自艾滋病临床试验的许多数据集提供了理想的设置 这种类型的合成。此应用程序的重点是开发 用于改进此类分析的必要的分层建模方法 数据集，以及相应的用户友好计算工具，以支持 非专家统计支持人员在临床试验中对方法的使用 数据协调中心。 在简要回顾了过去的几个发展之后，具体到以下三个领域 提出要对健康科学进行深入探索。首先， 艾滋病临床试验数据中期监测显示成果丰硕 适用范围。这一问题的全层次贝叶斯方法 指定具有适当损失的似然分布和先验分布 功能。研究人员将比较传统的分析方法， 反向归纳法，其前向采样算法基本上 减轻了分析和计算负担。第二个领域是 调查是使用分层模型进行高度分层 生存数据。完全参数方法(比例风险，威布尔 基线风险模型)很容易解释，同时允许 高度的模型灵活性。研究人员还将考虑一种 半参数模型，更灵活，但更难 具体说明和解释。建议研究的第三个领域是分析 来自几个艾滋病试验方案的数据同时在 多个临床单位。在这里，分层建模非常适合 结合信息以获得研究和研究的改进估计 特定于机组的型号参数。NIAID赞助的社区计划 爱滋病临床研究(CPCRA)，由加州大学协调 明尼苏达州，将提供丰富的数据来评估这些 分层方法。提出的三个具体问题领域不是 意在详尽无遗，但却表明了广泛的问题， 奠定了所有艾滋病临床试验分析的基础，以及未来不可避免的趋势 将分层建模理论与艾滋病相结合的机会 研究应用。

DESCRIPTION (adapted from the Abstract): Hierarchical models enable the combining of information from similar and independent experiments, yielding improved estimation of both individual and shared model characteristics. Many data sets arising from AIDS clinical trials offer ideal settings for this type of synthesis. This application is focused on developing the necessary hierarchical modeling methods for improved analysis of such datasets, as well as corresponding user-friendly computing tools to enable use of the methods by non-expert statistical support staff at clinical trial data coordinating centers. After a brief review of several past developments, three areas specific to the health sciences are proposed for in-depth exploration. First, the interim monitoring of AIDS clinical trials data is shown to be a fruitful area of application. A fully hierarchical Bayesian approach to this problem specifies the likelihood and prior distributions with appropriate loss functions. The researchers will compare the traditional analytic method, backward induction, with a forward sampling algorithm that substantially eases the analytic and computational burden. A second area for investigation is the use of hierarchical models for highly stratified survival data. A fully parametric approach (a proportional hazards, Weibull baseline hazard model) emerges as easily interpretable, while permitting a high degree of model flexibility. The researchers will also consider a semiparametric model which is more flexible, but somewhat more difficult to specify and interpret. A third area proposed for study is the analysis of data arising from several AIDS trial protocols in process simultaneously at a number of clinical units. Here, hierarchical modeling is ideal for combining information to obtain improved estimates of both study- and unit-specific model parameters. The NIAID-sponsored Community Programs for Clinical Research on AIDS (CPCRA), coordinated at the University of Minnesota, will provide a wealth of data on which to evaluate these hierarchical methods. The three specific proposed problem areas are not intended to be exhaustive, but rather indicative of the broad issues which underlie all AIDS clinical trial analyses, and the inevitable future stream of opportunities for combining hierarchical modeling theory with AIDS research application.