Statistical Methods for Combination HIV Prevention Approaches

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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.