Statistical Methods for Partially Controlled Studies

描述（由申请人提供）：本项目的目标是 开发新的统计设计和方法，以更好地评估部分 公共卫生的对照研究。 有三个具体目标。第一个目标是开发评估方法 研究中的治疗：不直接控制哪些受试者得到哪些治疗 治疗，或受试者提供的结果;但控制的位置， 提供治疗的网站。因为现有的方法，包括 标准工具变量，不能处理 接受治疗和提供结果的不可控因素， 根据目标1，对适当的治疗评价至关重要。这项工作将 在巴尔的摩进行的针具交换项目中， 传输第二个目标是为临床试验开发新的方法 接受的治疗得到控制，结果得到衡量; a 测量治疗后替代终点;目标是估计 治疗对结果的影响是并发的和不并发的 代孕妈妈目标2下的方法使用了一种新的分层方法， 不受控制的替代品，避免标准的并发症 治疗后变量的分层。这项工作是必要的适当 评价早期治疗后症状作为后期治疗的指标 我们将把它应用于癌症和眼科试验。第三个目标 是开发研究设计的方法，其中：一些人退出了一个 纵向随访，我们可以在哪里找到（双样本）代表性 辍学者的一个子集。这项工作对于保持普遍性至关重要 长期随访研究的结果，我们将应用它来更好地评估 青少年对药物依赖的时间。

DESCRIPTION (provided by applicant): The objective of this project is to develop new statistical designs and methods to better evaluate partially controlled studies in public health. There are three specific aims. The first aim is to develop methods to evaluate treatments in studies that: do not directly control which subjects get which treatment, or which subjects provide outcomes; but do control the location of the sites that provide the treatments. Because existing methods, including standard instrumental variables, cannot handle the coexistence of the uncontrolled factors of taking treatment and of providing outcomes, the work under aim 1 is essential for proper treatment evaluation. This work will be applied to ongoing needle exchange programs in Baltimore, for preventing HIV transmission. The second aim is to develop new methods for clinical trials where treatment received is controlled, and outcomes are measured; a post-treatment surrogate endpoint is measured; and the goal is to estimate the treatments effects on the outcome that are concurrent and not concurrent with the surrogate. The methods under aim 2 use a new type of stratification for the uncontrolled surrogates that avoid the complications of standard stratifications on post-treatment variables. This work is essential for proper evaluation of early post-treatment symptoms as measures of later treatment effect, and we will apply it to cancer and ophthalmology trials. The third aim is to develop methods for study designs where: some persons drop out of a longitudinal follow-up, and where we can find (double-sample) a representative subset of the dropouts. This work is essential for preserving generalizability of results from long follow-up studies, and we will apply it to better evaluate time to potential drug dependence in youths.