The objective of this methods proposal is to develop and apply new statistical methods to improve scientific inferences on the disease attribution associated with risk factors, such as obesity and tobacco smoking, for data collected from longitudinal cohort studies in chronic disease prevention research, specifically aiming to: (1) develop general statistical methods for time-varying attributable risk functions (ARF) to assess and compare prevention strategies;(2) develop statistical methods for a set of pairwise ARF's to assess and identify practically optimal prevention strategies;(3) develop statistical methods to evaluate the disease attribution in residual time;(4) develop statistical software and apply the developed methods to a large collection of cohort data from the Asia Cohort Consortium.
This application aims to develop new statistical tools to analyze the time-varying disease attribution for longitudinal cohort studies in chronic disease prevention research. Its overall public health impact lies in applying these new tools to improve our understanding of the health implications of prevention strategies in translational research and clinical care.
|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|
|Safren, Steven A; Mayer, Kenneth H; Ou, San-San et al. (2015) Adherence to Early Antiretroviral Therapy: Results From HPTN 052, a Phase III, Multinational Randomized Trial of ART to Prevent HIV-1 Sexual Transmission in Serodiscordant Couples. J Acquir Immune Defic Syndr 69:234-40|
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|Saegusa, Takumi; Di, Chongzhi; Chen, Ying Qing (2014) Hypothesis testing for an extended cox model with time-varying coefficients. Biometrics 70:619-28|