The Annals of Applied Statistics

Nonparametric inference for immune response thresholds of risk in vaccine studies

Kevin M. Donovan, Michael G. Hudgens, and Peter B. Gilbert

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An important objective in vaccine studies entails identifying an immune response which is predictive of disease risk. Nonparametric methods are developed for inference on immune response thresholds that are associated with specified levels of disease risk, including where the risk level is zero. This threshold is defined as the minimum immune response value above which disease risk is less than or equal to the desired level. The proposed nonparametric methods are compared to previously developed parametric methods in simulation studies. The methods are extended for use in studies that only measure the immune response in a subset of participants, such as case-cohort or case-control studies, and with right censored time to disease outcomes. Finally, these methods are used to estimate neutralizing antibody thresholds for virologically confirmed dengue risk using data from two recent dengue vaccine trials.

Article information

Ann. Appl. Stat., Volume 13, Number 2 (2019), 1147-1165.

Received: June 2018
Revised: December 2018
First available in Project Euclid: 17 June 2019

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Zentralblatt MATH identifier

Case-cohort sampling nonparametric risk threshold vaccine studies


Donovan, Kevin M.; Hudgens, Michael G.; Gilbert, Peter B. Nonparametric inference for immune response thresholds of risk in vaccine studies. Ann. Appl. Stat. 13 (2019), no. 2, 1147--1165. doi:10.1214/18-AOAS1237.

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Supplemental materials

  • R code for computation of nonparametric estimators for thresholds of risk. We provide a repository which includes R functions to compute the proposed nonparametric estimators, as well as code to recreate some of the simulation studies in this paper.