Statistical methods are critically important for advancing major areas of HIV/AIDS research. This proposal to NIAID aims to develop novel statistical approaches for the design and analysis of studies of the prevention and treatment of HIV infection and associated co-infections. It addresses a key objective of NIAID to help reduce the global impact of HIV/AIDS on society. The following specific aims will be addressed:
Aim 1. To develop statistical methods for prevention studies, including: (1.1) estimating HIV incidence using detuned assays, and (1.2) characterizing transmission in a community by estimating the probability of transmission between infected subjects.
Aim 2. To develop statistical methods for the design and analysis of HIV/AIDS therapeutic studies, in- cluding: (2.1) methods for the joint analysis of efficacy and safety data from HIV studies that handle potentially informative losses to follow-up, (2.2) models and methods for evaluating algorithms for the monitoring of CD4 count and viral load for treatment management, and (2.3) methods for deep sequencing data.
Aim 3. To develop statistical methods for the personalized treatment of HIV-infected patients using data from observational studies. The problems that will be addressed concern major topical issues being confronted in current collaborative research in the treatment and prevention of HIV and associated co-infections.
Under Aim 1, new study de- sign and analysis methods for more reliable estimation of HIV incidence will address methodological issues raised in reports by UNAIDS and the Institute of Medicine, and new methods will be developed to improve understanding of transmission pathways in communities so facilitating better targeting of prevention programs.
Under Aim 2, methods will be developed to allow better assessment of the efficacy and safety of treatments, to evaluate more affordable novel technologies for measuring CD4 counts or viral load that might be used in resource-limited settings (a key priority identified by the World Health Organization), and to advance the im- plementation of deep sequencing technologies for the detection of low frequency drug resistance mutations.
Under Aim 3, the proposed methods can be applied in the many large HIV observational studies to aid in identifying the best treatment for an individual HIV-infected patient. The ultimate goal is to develop practical approaches that can be readily implemented in HIV and co-infection research and, in doing so, help improve programs for the prevention of transmission and the treatment of HIV-infected people.
The research proposal addresses the pressing need for advanced statistical tools that solve study design and analysis challenges being encountered in current HIV research. The development of novel statistical methods that are easy to implement will aid in addressing these challenges and so further the prevention of transmission of HIV and the treatment of people infected with HIV and associated co-infections.
|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|
|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|
|Zhao, Lihui; Claggett, Brian; Tian, Lu et al. (2016) On the restricted mean survival time curve in survival analysis. Biometrics 72:215-21|
|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|
|Uno, Hajime; Tian, Lu; Claggett, Brian et al. (2015) A versatile test for equality of two survival functions based on weighted differences of Kaplan-Meier curves. Stat Med 34:3680-95|
|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|
|Wang, Rui; Schoenfeld, David A; Hoeppner, Bettina et al. (2015) Detecting treatment-covariate interactions using permutation methods. Stat Med 34:2035-47|
|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|
|Carnegie, Nicole Bohme; Wang, Rui; Novitsky, Vladimir et al. (2014) Linkage of viral sequences among HIV-infected village residents in Botswana: estimation of linkage rates in the presence of missing data. PLoS Comput Biol 10:e1003430|
|Wang, Rui; Goyal, Ravi; Lei, Quanhong et al. (2014) Sample size considerations in the design of cluster randomized trials of combination HIV prevention. Clin Trials 11:309-318|
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