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/艾滋病患者的计划进行准确评估是抗击这一流行病的核心。评估计划的一个主要障碍是患者辍学。一个重要的此类计划是总统艾滋病紧急救援计划(PEPFAR)。美国一直在赞助PEPFAR(2004-2013年为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，PEPFAR经历了很高的辍学率(例如，两年内辍学率为39%)。标准生存方法只使用观察到的非辍学数据，没有关于辍学的客观信息。在考虑到观察到的信息后，当辍学患者与非辍学患者不同时，这种方法可能会出现严重的偏见。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“双抽样”。这种设计将增加的资源重新分配给目标，密集地追逐和寻找辍学者的子集。这些双抽样辍学旨在代表非双抽样辍学，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，但我们早些时候已经表明，当双重抽样用于连续登记计划时，标准生存方法失败，例如PEPFAR。此外，我们早些时候已经证明，不进行双重抽样的标准评估可以将PEPFAR中的死亡率大大低估5倍。所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这一提议的潜在影响。拟议的新方法是在东非PEPFAR的推动下为三个具体目标制定的。(目标1)。制定方法，通过使用给定的双抽样设计的数据来评估后续计划的性能。在这个目标中，我们将开发双抽样设计的方法来估计存活率，双抽样设计根据患者退出前的病史特征来选择患者。这对于接下来的两个目标也很重要。(目标2)。开发方法来创建双抽样设计，以便在给定固定资源的情况下对程序的性能进行最准确的估计。证据表明，特定于患者的信息对于双重抽样设计提供关于方案的最佳信息是重要的。在这里，我们将创建依赖于患者的双抽样设计，在给定资源的情况下最大限度地提高在此类计划中估计生存的准确性。(目标3)。开发双抽样设计，以最大限度地瞄准临床目标。目的1可以利用辍学患者的临床病史来预测死亡风险最高的人群。这些预测可能会限制设计，以确保对所有此类患者进行双重抽样，以便更好地为他们提供医疗服务。在目标3中，我们将创建最大限度地提高估计准确性并使患者受益的设计。

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.