Statistical Designs and Methods for Double-Sampling for HIV/AIDS

描述（由申请人提供）：对世界各地治疗和监测艾滋病毒/艾滋病患者的项目进行准确评估，对于抗击这一流行病至关重要。项目评估的一个主要障碍是患者退出。其中一个重要的项目是总统艾滋病紧急救援计划（PEPFAR）。美国一直在资助PEPFAR（2004-2013年630亿美元），准确估计患者生存的方法是指导美国管理该计划的核心。然而，总统防治艾滋病紧急救援计划的辍学率很高（例如，两年内高达39%）。标准生存方法仅使用观察到的非辍学数据，没有关于辍学的客观信息。考虑到观察到的信息后，当辍学患者与非辍学患者不同时，这种方法可能存在严重偏差。为了提供有效的评估，在早期的工作中，我们使用了一种更丰富的设计，称为“双重抽样”。这种设计将增加的资源重新分配给目标，集中追求和寻找辍学者的子集。这些双抽样的退出者旨在代表非双抽样的退出者，并为整个队列提供客观信息。虽然标准方法在调查中以双重抽样而闻名，但我们之前已经表明，在诸如PEPFAR等连续登记项目中使用双重抽样时，标准生存方法失效。此外，我们早些时候已经表明，没有双重抽样的标准评估可能会大大低估PEPFAR的死亡率，低估幅度为5倍。提出的方法将建立在我们早期的“主要分层”框架下的工作基础上。该框架的成功增加了本提案的潜在影响。提出的新方法是为三个具体目标而开发的，其动机是在东非的PEPFAR。(目标1)。通过使用给定的双抽样设计的数据，开发评估后续程序性能的方法。在这个目标中，我们将开发方法来估计生存从双抽样设计，选择患者根据他们的历史特征在退出前。这对接下来的两个目标也很重要。(目标2)。开发方法来创建双重抽样设计，在给定固定资源的情况下，对程序的性能产生最准确的估计。有证据表明，患者的特定信息对于双抽样设计提供有关程序的最佳信息非常重要。在这里，我们将创建患者依赖的双重抽样设计，以最大限度地提高给定资源的准确性，以估计此类计划中的存活率。(3)为目标。发展双重抽样设计，以达到最佳的临床目标。Aim 1可以利用辍学患者的临床病史来预测死亡风险最高的患者。这些预测可以约束设计，以确保对所有这些患者进行双重抽样，以更好地为他们提供医疗服务。在目标3中，我们将创建最大化估计准确性和最佳造福患者的设计。

DESCRIPTION (provided by applicant): Accurate evaluation of programs that treat and monitor HIV/AIDS patients around the world is central for fighting the epidemic. A major obstacle for program evaluation is patient dropout. An important such program is the President's Emergency Plan for AIDS Relief (PEPFAR). The US has been sponsoring PEPFAR ($63 billion for 2004-2013), and methods to accurately estimate patient survival are central to guide US management of the program. However, PEPFAR experiences high dropout rates (e.g., 39% in two years). Standard survival methods use only the observed non-dropout data, with no objective information for the dropouts. Such methods can be severely biased when dropout patients differ from nondropouts after accounting for observed information. To provide valid evaluation, in earlier work we have used a richer design known as "double-sampling". This design re-allocates increased resources to target, intensively pursue and find a subset of the dropouts. These double-sampled dropouts are intended to represent the non-double-sampled dropouts, and to provide objective information for the entire cohort. Although standard methods have been known for double-sampling in surveys, we have shown earlier that standard survival methods fail when double-sampling is used in continuous enrollment programs such as PEPFAR. Also, we have shown earlier that standard evaluation without double-sampling can dramatically underestimate mortality in PEPFAR by a factor of 5. The proposed methods will build on our earlier work with the framework of "principal stratification". The success of that framework increases the potential impact of this proposal. The proposed new methods are developed for three specific aims, motivated by PEPFAR in East Africa. (Aim 1). Develop methods to estimate the performance of follow-up programs by using data from a given double- sampling design. In this aim we will develop methods to estimate survival from double-sampling designs that select patients based on their history characteristics before dropout. This is also important for the next two aims. (Aim 2). Develop methods to create double-sampling designs that produce most accurate estimation of a pro- gram's performance given fixed resources. Evidence shows that information specific to a patient is important for what double-sampling designs provide best information about a program. Here, we will create patient-dependent double-sampling designs that maximize the accuracy given resources to estimate survival in such programs. (Aim 3). Develop double-sampling designs to best target clinical goals. Aim 1 can use the dropout patients' clinical history to predict those with highest mortality risk. These predictions can constrain the design to ensure to double-sample all such patients to better serve them medically. In Aim 3, we will create designs that maximize the accuracy of estimation and best benefit patients.