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.

Keywords:

• Competing risks
• Dependent censoring
• HIV
• Machine learning
• Malaria
• Targeted minimum loss-based estimation
• Vaccine

Acknowledgments

The authors thank the participants, investigators, and sponsors of the RV144 and RTS,S/AS01 trials. For RV144 these include Morgane Rolland and the U.S. Military HIV Research Program (MHRP); U.S. Army Medical Research and Materiel Command; NIAID; U.S. and Thai Components, Armed Forces Research Institute of Medical Science Ministry of Public Health, Thailand; Mahidol University; SanofiPasteur; and Global Solutions for Infectious Diseases. For RTS,S/AS01 these include Christian Ockenhouse for the Path Malaria Vaccine Initiative; Dyann Wirth for the Harvard School of Public Health; GlaxoSmithKline; and the Broad Institute Genomics Platform. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The views expressed are those of the authors and should not be construed to represent the positions of the U.S. Army or the Department of Defense.

Additional information

Funding

David Benkeser was partially supported by Bill and Melinda Gates Foundation grant OPP1147962. Marco Carone was partially supported by NIH funding: NIAID grant 3UM1AI068635-09 and by the Career Development Fund of the Department of Biostatistics at the University of Washington. Peter Gilbert was partially supported by contract # 792087 from the Henry Jackson Foundation for the MHRP and by NIH funding: NIAID grant R37AI054165.