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.
This application proposes statistical methods development for HIV combination prevention approaches. The proposed development aims to provide a set of useful tools for design, analysis and implementation of HIV combination prevention approaches. Its translational value lies in that these tools shall greatly facilitate the search and the assessment of optimal HIV combination prevention approaches to achieve maximal benefit in HIV prevention.
|Wang, Ching-Yun; Dieu Tapsoba, Jean De; Duggan, Catherine et al. (2016) Methods to adjust for misclassification in the quantiles for the generalized linear model with measurement error in continuous exposures. Stat Med 35:1676-88|
|Crouch, Luis Alexander; May, Susanne; Chen, Ying Qing (2016) On estimation of covariate-specific residual time quantiles under the proportional hazards model. Lifetime Data Anal 22:299-319|
|Tang, Zhenzhu; Lan, Guanghua; Chen, Ying Qing et al. (2015) HIV-1 Treatment-as-Prevention: A Cohort Study Analysis of Serodiscordant Couples in Rural Southwest China. Medicine (Baltimore) 94:e902|