Open Access
June 2019 Nonparametric inference for immune response thresholds of risk in vaccine studies
Kevin M. Donovan, Michael G. Hudgens, Peter B. Gilbert
Ann. Appl. Stat. 13(2): 1147-1165 (June 2019). DOI: 10.1214/18-AOAS1237

Abstract

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.

Citation

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Kevin M. Donovan. Michael G. Hudgens. Peter B. Gilbert. "Nonparametric inference for immune response thresholds of risk in vaccine studies." Ann. Appl. Stat. 13 (2) 1147 - 1165, June 2019. https://doi.org/10.1214/18-AOAS1237

Information

Received: 1 June 2018; Revised: 1 December 2018; Published: June 2019
First available in Project Euclid: 17 June 2019

zbMATH: 1423.62139
MathSciNet: MR3963566
Digital Object Identifier: 10.1214/18-AOAS1237

Keywords: Case-cohort sampling , nonparametric , risk threshold , vaccine studies

Rights: Copyright © 2019 Institute of Mathematical Statistics

Vol.13 • No. 2 • June 2019
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