Novel Statistical Methods for Cluster-Randomized HIV Prevention Trials

项目总结/摘要 艾滋病毒在美国和撒哈拉以南大部分地区得不到充分服务的人群中仍然普遍存在 非洲，促使公共卫生部门大力关注艾滋病毒预防做法和暴露前预防措施的结合， 预防（PrEP）。随机对照试验（CRT）是这些HIV预防方法研究的组成部分。 通过集体随机分组的个人到相同的治疗分配，群集随机设计 最大限度地减少试验组之间的污染，并使社区一级预防工作的研究可行。 逐步楔形分组随机试验（SW-CRT）是这种设计的最新改良，涉及单因素， 从标准治疗到干预治疗的区域交叉。然而，聚类随机化导致相关性 在同一个集群中的观察之间，这些观察经常被重复评估， 来的兴趣。用于临床试验设计和监测的现有统计方法未能解决这种复杂的问题。 数据结构，限制了研究人员进行统计学上严格的艾滋病毒预防CRT的能力。 该提案通过以下方式解决了这一方法上的差距：（1）为CRT制定临时监测方法 区间删失终点和（2）开发多个SW-CRT的功效和样本量方法 聚类和纵向跟踪的水平。间隔删失发生在艾滋病毒预防试验中，当时间- 仅在间歇性门诊访视时评估关注的事件前结局。虽然存在临时监测方法， 对于集群数据和事件发生时间数据，目前没有方法同时处理集群数据和时间间隔数据。 审查意见。中期监测常用的一种统计工具是条件把握度， 在试验结束时检测到显著预防效果的条件概率，考虑到观察到的 中期数据和一组关于研究剩余部分的假设。Aim 1提出了一种基于模拟的 计算具有区间删失终点的CRT的条件把握度的方法。 目标2开发具有分层聚类和纵向的SW-CRT的样本量和功效公式 随访这种依赖性结构在艾滋病毒预防研究中自然出现，例如， 这些病人被集中在医院里的医生中，然后随着时间的推移被监控， 艾滋病血清转化。无样本量或功效方法直接用于阶梯楔形集随机试验 解决这个数据结构。因此，目前的阶梯楔形试验可能不适当， 研究人员难以检测相关的预防效果。 因此，根据这项建议制定的方法将提高进行创新、 需要进行随机分组的HIV预防研究，以评估 在高危人群中开展PrEP，并确定实施艾滋病毒综合预防战略的最佳方式。

Project Summary/Abstract HIV remains prevalent among underserved populations in the United States and large parts of sub-Saharan Africa, prompting a strong public health focus on combination HIV prevention practices and pre-exposure pro- phylaxis (PrEP). Cluster-randomized trials (CRTs) are integral to the study of these HIV prevention methods. By collectively randomizing groups of individuals to the same treatment assignment, cluster-randomized designs minimize contamination between trial arms and make feasible the study of community-level prevention efforts. Stepped-wedge cluster-randomized trials (SW-CRTs) are a recent modification of this design involving unidi- rectional crossover from standard-of-care to intervention. However, cluster randomization induces correlation between observations in the same cluster, and these observations are often repeatedly assessed for the out- come of interest. Existing statistical methods for clinical trial design and monitoring fail to address such complex data structures, limiting investigators' ability to conduct statistically rigorous CRTs for HIV prevention. This proposal addresses this methodological gap by (1) developing interim monitoring methods for CRTs with interval-censored endpoints and (2) developing power and sample size methods for SW-CRTs with multiple levels of clustering and longitudinal follow-up. Interval-censoring occurs in HIV prevention trials when the time- to-event outcome of interest is assessed only at intermittent clinic visits. While interim monitoring methods exist for clustered data and for time-to-event data separately, no methods currently handle both clustered and interval- censored observations. One statistical tool commonly used for interim monitoring is the conditional power, the conditional probability of detecting a significant preventative effect at the end of the trial given both the observed interim data and a set of assumptions about the remainder of the study. Aim 1 proposes a simulation-based approach to calculating the conditional power of CRTs with interval-censored endpoints. Aim 2 develops sample size and power formulae for SW-CRTs with hierarchical clustering and longitudinal follow-up. Such dependence structures arise naturally in HIV prevention studies, as, for example, patients are clustered within physicians who are grouped within hospitals, and these patients are then monitored over time for HIV seroconversion. No sample size or power methods for stepped-wedge cluster-randomized trials directly address this data structure. Current stepped-wedge trials may be inappropriately powered as a result, making it difficult for investigators to detect pertinent prevention effects. Thus, the methods developed under this proposal will improve the feasibility of conducting the innovative, cluster-randomized HIV prevention studies needed to evaluate the effectiveness of and patient adherence to PrEP in at-risk populations, and to determine the best ways to implement combination HIV prevention strategies.