Statistical Methods for Combination HIV Prevention Approaches

描述（由申请人提供）：本申请是对资助机会公告（FOA）RFA-MH-14-180的回应，标题为“联合艾滋病毒预防方法的方法和形成性工作（R 01）”，旨在为设计，分析和实施联合艾滋病毒预防方法中的挑战性问题制定统计方法。在申请中，我们确定了艾滋病综合预防研究人员、政策制定者和从业人员面临的四个紧迫挑战：1）寻找强有力且易于实施的试验设计来评估综合预防干预措施的有效性; 2）评估综合预防方法的公共卫生影响; 3）利用已建立的政府监测系统监测资源使用情况，改进方案执行情况，并衡量综合预防干预措施的影响;评估复杂流行病模型的预测，特别是当它们可能用于规划联合干预研究时。为了应对这些挑战，我们的目标是开发统计方法：1）因子设计与一个灵活的一般回归模型的删失时间到事件的结果，2）随时间变化的归因风险函数，以评估综合预防干预覆盖面的公共卫生影响，3）使用监测数据，以告知在社区一级的综合预防的状态，以及4）评估模型预测以告知正在进行的HIV联合预防临床试验。在完成拟议的工作后，拟制定的方法应提供一套有用的统计工具，以评估、比较、通报和预测艾滋病毒综合预防方法。

DESCRIPTION (provided by applicant): This application is in response to the Funding Opportunity Announcement (FOA) RFA-MH-14-180, entitled ''Methodologies and Formative Work for Combination HIV Prevention Approaches (R01),'' aiming to develop statistical methods for challenging issues in design, analysis and implementation of combination HIV prevention approaches. In the application, we identify four imminent challenges faced by researchers, policy makers and practitioners on HIV combination prevention: 1) searching powerful and easy-to-implement trial design(s) to assess the effectiveness of combined prevention interventions; 2) assessing the public health impact of a combination prevention approach; 3) utilizing established government surveillance systems to monitor resource use, improve program implementation and measure impact of combination prevention interventions; and 4) evaluating the predictions of complex epidemic models, particularly as they may be used in planning of combination intervention studies. To address these challenges, we aim to develop statistical methods for: 1) factorial design with a flexible general regression model for censored time-to-event outcomes,; 2) time-varying attributable risk function to assess the public health impact of combination prevention intervention coverage; 3) the use of surveillance data to inform status of combination prevention at community level; and 4) evaluating model prediction to inform ongoing HIV combination prevention clinical trials. Upon completion of the proposed work, the methods to be developed shall provide a useful set of statistical tools to assess, compare, inform and predict HIV combination prevention approaches.