The long-term goals of this grant are to develop statistical models and methods for analyzing AIDS clinical trials and natural history studies. The proposed research on assessing drug compliance (Specific Aim 1) using longitudinal data on biological markers is aimed at better characterizing the compliance to study medication in clinical trials. This information would aid in the interpretation of the findings of AIDS trials in which noncompliance to prescribed protocol therapies occurred. The propose research in Specific Aim 2 aims to determine optimal designs for assessing the development of HIV resistance and to develop methods for assessing the association between the onset of resistance and either the risk of clinical progression or a change in the rate of decline in a marker such as CD4 cell count. The proposed research on methods for analyzing dependently- censored survival data is aimed at identifying methods that can be used to compare treatment groups when failure times are censored for reasons (e.g., patient voluntary withdrawal) that may be related to their prognosis, and for evaluating the properties of these methods. The proposed research on methods for analyzing marker data (Specific Aim 3) was motivated by several ongoing AIDS trials in which patient's treatment arms are modified when their CD4 cell counts drop below a certain threshold, and where it there is interest in evaluating the effects of this modification. The proposed research on nonlinear growth curves is aimed at developing methods for evaluating the developmental aspects of HIV-infected children and the effects on development from their treatments.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI024643-08
Application #
2062663
Study Section
AIDS and Related Research Study Section 2 (ARRB)
Project Start
1987-04-01
Project End
1996-10-31
Budget Start
1994-11-01
Budget End
1995-10-31
Support Year
8
Fiscal Year
1995
Total Cost
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
082359691
City
Boston
State
MA
Country
United States
Zip Code
02115
Lok, Judith J; Yang, Shu; Sharkey, Brian et al. (2018) Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms. Lifetime Data Anal 24:201-223
Angelidou, Konstantia; Palumbo, Paul; Lindsey, Jane et al. (2018) Defining Study Outcomes That Better Reflect Individual Response to Treatment. Pediatr Infect Dis J 37:258-262
Harling, Guy; Wang, Rui; Onnela, Jukka-Pekka et al. (2017) Leveraging contact network structure in the design of cluster randomized trials. Clin Trials 14:37-47
Wang, Rui; De Gruttola, Victor (2017) The use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials. Stat Med 36:2831-2843
Zhao, Lihui; Claggett, Brian; Tian, Lu et al. (2016) On the restricted mean survival time curve in survival analysis. Biometrics 72:215-21
Zhang, Yifan; Trippa, Lorenzo; Parmigiani, Giovanni (2016) Optimal Bayesian adaptive trials when treatment efficacy depends on biomarkers. Biometrics 72:414-21
Prague, Melanie; Wang, Rui; Stephens, Alisa et al. (2016) Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster-randomized trials with missing outcomes. Biometrics 72:1066-1077
Li, Junlong; Zhao, Lihui; Tian, Lu et al. (2016) A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies. Biometrics 72:877-87
Lok, Judith J; Hughes, Michael D (2016) Evaluating predictors of competing risk outcomes when censoring depends on time-dependent covariates, with application to safety and efficacy of HIV treatment. Stat Med 35:2183-94
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

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