A model for immunological correlates of protection

Abstract

Immunological assays measure characteristics of the immune system, such as antibody levels, specific to certain diseases. High assay values are often associated with protection from disease. A question of interest is how the relationship between assay values and subsequent development of disease should be quantitatively modelled. Existing approaches successfully model the relationship for high assay values, where the probability of developing disease is low. However at low assay values, the probability of developing disease is more closely associated with factors such as disease prevalence rates and an individual's chance of exposure to infection; these are less well captured by existing models. This paper presents a model that accommodates both assay values and factors independent of assay values, enabling protection from disease to be modelled over the whole range of assay values and proposing a method for predicting the efficacy of a vaccine from the assays of vaccinees and non-vaccinees.

Correlates of protection for rotavirus vaccines: Possible alternative trial endpoints, opportunities, and challenges

“…15 Discussions in the literature on this subject have been frequently based on postulates for evaluating surrogate trial endpoints proposed in 1989 by Prentice. 8,10,16,17 Prentice's first condition is that a valid surrogate endpoint must be correlated with the true clinical endpoint.…”

“…9,20 Statistical analyses for individual VE trials In numerous vaccine studies, a threshold level of an immune assay above which subjects do not develop disease have been identified. 15 Individuals who achieve the threshold after vaccination are considered protected. Associations between protection and a minimal threshold antibody titer are usually analyzed using the Chi-square test (x 2 ) or Fisher's exact test.…”

“…Dunning,15 are: 1) The relationship between assay values and the occurrence of disease below the threshold level is frequently not specified. In this case, the rate of disease among individuals with low immune assay values may be more strongly associated with the risk of exposure and disease prevalence rates, variables not taken into account in the threshold model.…”

“…2) Subjects with assay values above a chosen threshold will occasionally develop disease. Taking into account these issues, models for calculating titer-specific rates of disease 22 or that complete the unspecified part of the threshold model 15 have been developed. Logistic regression is also used to quantify the relationship between different immune assay values and the occurrence of disease.…”

Validation and evaluation of serological correlates of protection for inactivated enterovirus 71 vaccine in children aged 6-35 months

“…In this study, we aimed to assess the relationship between NTAb titers post-vaccination and the occurrence of subsequent EV71-associated disease by fitting the Prentice framework [13][14][15][16] and to estimate the mechanistic correlateof protection (mCoP) 17 using the scaled logit model developed by Dunning 18 based on the data collected from the whole cohort in a randomized, double-blind, placebo-control trial of inactivated EV71 vaccine. 10 …”