Development of a protective HIV vaccine would have great impact in stemming the HIV pandemic. Placebo-controlled preventative HIV vaccine efficacy trials and controlled non-human primate (NHP) vaccine challenge trials are important components of vaccine development. This renewal application proposes to continue development of novel statistical methods to address key issues in the design and analysis of such trials.
Aim 1 (""""""""Sieve Analysis"""""""") develops statistical methods for evaluating the impact of HIV genotypic and phenotypic variation on the effects of HIV vaccines evaluated in efficacy trials. The methods build on previous work to address three key challenges: (1) ideally the sieve analysis assesses HIV sequences measured on acute-phase samples of breakthrough infected subjects, yet for some subjects only post-acute-phase sequences can be measured;(2) the analysis of longitudinal HIV sequences is needed to discriminate whether an observed difference in sequences (vaccine-vs-placebo) is due to strain-specific blockage of HIV acquisition or to post-acquisition T-cell driven HIV sequence evolution;and (3) achieving high statistical power requires that the measure of genetic distance between a subject's breakthrough sequences and the vaccine-insert sequence be highly immunologically relevant, based on T cell epitopes that a subject can recognize given his/her HLA alleles.
Aim 2 (""""""""Immune Correlates"""""""") develops statistical methods for evaluating vaccine-induced immune responses as surrogates of protection within vaccine efficacy trials. The methods build on previous work for evaluating an immunological surrogate endpoint, with new directions to: evaluate and compare surrogate value of multiple immunological biomarkers;improve efficiency by leveraging baseline covariates;improve robustness by weakening assumptions;develop an innovative one-way crossover design;provide assumption-diagnostics and sensitivity analyses;and develop meta-analysis methods for evaluating immunological surrogates over multiple efficacy trials. The methods will be applied to the six HIV vaccine efficacy trials that have been conducted. The meta-analysis methods will also be employed to predict efficacy of influenza A H1N1 vaccines.
Aim 3 (""""""""NHP Trials"""""""") develops statistical methods for evaluating vaccine efficacy on SIV acquisition, viral load, and survival endpoints, and for evaluating immunological surrogates for these endpoints within NHP trials. The methods (1) provide exact estimation and testing of vaccine efficacy, leveraging baseline covariates to improve efficiency;(2) address the challenge that viral load trajectories are """"""""truncated by death;"""""""" and (3) utilize novel study designs for evaluating surrogate endpoints. This research will be developed in collaboration with international leaders in pre-clinical HIV vaccine evaluation, and will be applied to several large NHP repeated low-dose challenge trials.

Public Health Relevance

Development of a protective HIV vaccine would have great impact in stemming the HIV pandemic. Three research areas central to developing a vaccine are the evaluation of how vaccine efficacy depends on HIV sequence variability, the evaluation of whether and how measurements of vaccine-induced immune responses predict vaccine efficacy, and the evaluation of vaccine efficacy in the non-human primate (NHP) model. For making progress in these areas, this proposal develops novel statistical methods for the design and analysis of preventative HIV vaccine efficacy trials and of NHP challenge trials.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Method to Extend Research in Time (MERIT) Award (R37)
Project #
5R37AI054165-10
Application #
8242601
Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Gezmu, Misrak
Project Start
2003-04-01
Project End
2015-03-31
Budget Start
2012-04-01
Budget End
2013-03-31
Support Year
10
Fiscal Year
2012
Total Cost
$311,664
Indirect Cost
$75,248
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
Huang, Ying (2017) Evaluating principal surrogate markers in vaccine trials in the presence of multiphase sampling. Biometrics :
Yang, Guangren; Sun, Yanqing; Qi, Li et al. (2017) Estimation of Stratified Mark-Specific Proportional Hazards Models under Two-Phase Sampling with Application to HIV Vaccine Efficacy Trials. Stat Biosci 9:259-283
Fu, Rong; Gilbert, Peter B (2017) Joint modeling of longitudinal and survival data with the Cox model and two-phase sampling. Lifetime Data Anal 23:136-159
Huang, Ying; Gilbert, Peter B; Fu, Rong et al. (2017) Statistical methods for down-selection of treatment regimens based on multiple endpoints, with application to HIV vaccine trials. Biostatistics 18:230-243
Fiore-Gartland, Andrew; Kullman, Nicholas; deCamp, Allan C et al. (2017) SieveSifter: a web-based tool for visualizing the sieve analyses of HIV-1 vaccine efficacy trials. Bioinformatics 33:2386-2388
Qi, Li; Sun, Yanqing; Gilbert, Peter B (2017) Generalized semiparametric varying-coefficient model for longitudinal data with applications to adaptive treatment randomizations. Biometrics 73:441-451
Spieker, Andrew J; Huang, Ying (2017) A method to address between-subject heterogeneity for identification of principal surrogate markers in repeated low-dose challenge HIV vaccine studies. Stat Med 36:4071-4080
Gilbert, Peter B; Juraska, Michal; deCamp, Allan C et al. (2017) Basis and Statistical Design of the Passive HIV-1 Antibody Mediated Prevention (AMP) Test-of-Concept Efficacy Trials. Stat Commun Infect Dis 9:
Sun, Yanqing; Qian, Xiyuan; Shou, Qiong et al. (2017) Analysis of two-phase sampling data with semiparametric additive hazards models. Lifetime Data Anal 23:377-399
Gilbert, Peter B; Huang, Ying (2016) Predicting Overall Vaccine Efficacy in a New Setting by Re-Calibrating Baseline Covariate and Intermediate Response Endpoint Effect Modifiers of Type-Specific Vaccine Efficacy. Epidemiol Methods 5:93-112

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