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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH105857-03
Application #
9054935
Study Section
Special Emphasis Panel (ZRG1-AARR-F (52))
Program Officer
Gordon, Christopher M
Project Start
2014-07-06
Project End
2019-02-28
Budget Start
2016-03-01
Budget End
2017-02-28
Support Year
3
Fiscal Year
2016
Total Cost
$488,577
Indirect Cost
$161,744
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
Huang, Yijian; Wang, Ching-Yun (2018) Cox regression with dependent error in covariates. Biometrics 74:118-126
Yu, Hsiang; Cheng, Yu-Jen; Wang, Ching-Yun (2018) Methods for multivariate recurrent event data with measurement error and informative censoring. Biometrics 74:966-976
Crouch, Luis Alexander; Zheng, Cheng; Chen, Ying Qing (2017) Estimating a Treatment Effect in Residual Time Quantiles under the Additive Hazards Model. Stat Biosci 9:298-315
Wang, Ching-Yun; Cullings, Harry; Song, Xiao et al. (2017) Joint nonparametric correction estimator for excess relative risk regression in survival analysis with exposure measurement error. J R Stat Soc Series B Stat Methodol 79:1583-1599
Zhao, Wei; Chen, Ying Qing; Hsu, Li (2017) On estimation of time-dependent attributable fraction from population-based case-control studies. Biometrics 73:866-875
Yu, Hsiang; Cheng, Yu-Jen; Wang, Ching-Yun (2016) Semiparametric Regression Estimation for Recurrent Event Data with Errors in Covariates under Informative Censoring. Int J Biostat 12:
Wang, Ching-Yun; Tapsoba, Jean De Dieu; 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
Wang, Ching-Yun; Song, Xiao (2016) Robust best linear estimator for Cox regression with instrumental variables in whole cohort and surrogates with additive measurement error in calibration sample. Biom J 58:1465-1484
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