Sieve Analysis: Statistical Methods for Assessing Genotype-Specific Vaccine Protection in HIV-1 Efficacy Trials with Multivariate and Missing Genotypes
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The extensive diversity of the human immunodeficiency virus type 1 (HIV-1) poses a major challenge for the design of a successful preventive HIV-1 vaccine. Thus an important component of HIV-1 vaccine development is the assessment of the impact of HIV-1 diversity on vaccine protection against HIV-1 acquisition. Statistical methods to evaluate whether and how vaccine efficacy depends on genetic features of exposing viruses in data collected in randomized double-blinded placebo-controlled Phase IIb/III preventive HIV-1 vaccine efficacy trials are developed. To characterize exposing HIV-1 strains, their genetic distances to the multiple HIV-1 sequences included in the vaccine construct are measured, where the set of genetic distances is considered as the continuous multivariate `mark' observable in infected subjects only. A mark-specific vaccine efficacy model is described in the framework of competing risks failure time analysis that allows improved efficiency of estimation, relative to current alternative approaches, by using the semiparametric method of maximum profile likelihood estimation in the vaccine-to-placebo mark density ratio model. In addition, the model allows to employ a more efficient estimation method for the overall hazard ratio in the Cox model. Mark data proximal to the time of HIV-1 acquisition, that are of greatest biological relevance, are commonly subject to missingness due to the intra-host HIV-1 evolution. Two inferential approaches accommodating missing marks are proposed: (i) weighting of the complete cases by the inverse probabilities of observing the mark of interest (Horvitz and Thompson, 1952), and (ii) augmentation of the inverse probability weighted estimating functions for improved efficiency and model robustness by leveraging auxiliary information predictive of the mark (using the general theory of Robins, Rotnitzky, and Zhao (1994)). The missing-mark methods provide a general framework for parameter estimation in density ratio/biased sampling models in the presence of missing data. The proposed methodology can serve either to make inference about whether and how vaccine efficacy varies with prespecified genetic distance measures, or as an exploratory tool to identify distance definitions with the greatest decline in vaccine efficacy, characterizing potential correlates of immune protection and indicating pathways for improved HIV-1 vaccine design. The developed methods are applied to HIV-1 sequence data collected in the RV144 Phase III preventive HIV-1 vaccine efficacy trial.
- Biostatistics