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Estimating and Testing Vaccine Sieve Effects Using Machine Learning

Abstract When available, vaccines are an effective means of disease prevention. Unfortunately, efficacious vaccines have not yet been developed for several major infectious diseases, including HIV and malaria. Vaccine sieve analysis studies whether and how the efficacy of a vaccine varies with the genetics of the pathogen of interest, which can guide subsequent vaccine development and deployment. In sieve analyses, the effect of the vaccine on the cumulative incidence corresponding to each of several possible genotypes is often assessed within a competing risks framework. In the context of clinical trials, the estimators employed in these analyses generally do not account for covariates, even though the latter may be predictive of the study endpoint or censoring. Motivated by two recent preventive vaccine efficacy trials for HIV and malaria, we develop new methodology for vaccine sieve analysis. Our approach offers improved validity and efficiency relative to existing approaches by allowing covariate adjustment through ensemble machine learning. We derive results that indicate how to perform statistical inference using our estimators. Our analysis of the HIV and malaria trials shows markedly increased precision—up to doubled efficiency in both trials—under more plausible assumptions compared with standard methodology. Our findings provide greater evidence for vaccine sieve effects in both trials. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.