Great efforts have been made over the past 20 years in HIV-related research. The results have provided useful information regarding the patient's disease management based on the individual patient characteristics. However, most of such """"""""personalized medicine"""""""" strategies were obtained on an ad hoc basis. There are numerous examples of subgroup analyses that could not be validated. Novel quantitative methods are needed to systematically develop sound treatment strategies at a personal level. For example, on average, the highly active antiretroviral therapy (HAART) is a very potent therapy for treating HIV-infection. However, not everyone benefits from HAART. Using data from clinical trials and observational studies, it is important to establish prediction models that can identify future patients who would benefit from such potent therapies. One of the main themes for this current research plan is to systematically develop quantitative methods to make valid inferences for future patient responses. There are five research aims in the proposal. The first three specifically address the personalized medicine issues. The last two aims are to develop new methods for designing, monitoring, and analyzing HIV studies with event time responses, which are commonly used endpoints for evaluating new therapies. The PI has assembled a research team which has been very productive in developing theoretically sound and practical quantitative methods for designing, monitoring and analyzing HIV- related clinical trials and observational studies over the past funding periods. The PI and co-investigators are also senior statisticians for the Center for Biostatical AIDS Research Center at Harvard University. They are intimately involved in HIV studies via the AIDS Clinical Trails Group (ACTG) and Neurologic AIDS research Consortium (NARC). They have access to the data from past and current ACTG and NARC studies, and can directly apply the results from developed methodologies to the on-going and future HIV studies.

Public Health Relevance

Quantitative methods will be developed for personalized medicine approaches for patient management of HIV disease using the data from clinical trials and observational studies. New statistical procedures will also be developed for designing, monitoring, and analyzing clinical trials for evaluating new therapies for HIV-infection.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
Project #
Application #
Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Gezmu, Misrak
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Harvard University
Biostatistics & Other Math Sci
Schools of Public Health
United States
Zip Code
Claggett, Brian; Tian, Lu; Castagno, Davide et al. (2015) Treatment selections using risk-benefit profiles based on data from comparative randomized clinical trials with multiple endpoints. Biostatistics 16:60-72
Shen, Yuanyuan; Liao, Katherine P; Cai, Tianxi (2015) Sparse kernel machine regression for ordinal outcomes. Biometrics 71:63-70
Tian, Lu; Zhao, Lihui; Wei, L J (2014) Predicting the restricted mean event time with the subject's baseline covariates in survival analysis. Biostatistics 15:222-33
Uno, Hajime; Claggett, Brian; Tian, Lu et al. (2014) Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. J Clin Oncol 32:2380-5
Matsouaka, Roland A; Li, Junlong; Cai, Tianxi (2014) Evaluating marker-guided treatment selection strategies. Biometrics 70:489-499
Parast, Layla; Tian, Lu; Cai, Tianxi (2014) Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial. J Am Stat Assoc 109:384-394
Zhao, Lihui; Tian, Lu; Cai, Tianxi et al. (2013) EFFECTIVELY SELECTING A TARGET POPULATION FOR A FUTURE COMPARATIVE STUDY. J Am Stat Assoc 108:527-539
Zhao, Lihui; Hu, X Joan (2013) Estimation with Right-Censored Observations Under A Semi-Markov Model. Can J Stat 41:237-256
Zhou, Qian M; Zheng, Yingye; Cai, Tianxi (2013) Subgroup specific incremental value of new markers for risk prediction. Lifetime Data Anal 19:142-69
Parast, Layla; Cai, Tianxi (2013) Landmark risk prediction of residual life for breast cancer survival. Stat Med 32:3459-71

Showing the most recent 10 out of 25 publications