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Some extensions in continuous models for immunological correlates of protection

Overview of attention for article published in BMC Medical Research Methodology, December 2015
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Title
Some extensions in continuous models for immunological correlates of protection
Published in
BMC Medical Research Methodology, December 2015
DOI 10.1186/s12874-015-0096-9
Pubmed ID
Authors

Andrew J. Dunning, Jennifer Kensler, Laurent Coudeville, Fabrice Bailleux

Abstract

A scaled logit model has previously been proposed to quantify the relationship between an immunological assay and protection from disease, and has been applied in a number of settings. The probability of disease was modelled as a function of the probability of exposure, which was assumed to be fixed, and of protection, which was assumed to increase smoothly with the value of the assay. Some extensions are here investigated. Alternative functions to represent the protection curve are explored, applications to case-cohort designs are evaluated, and approaches to variance estimation compared. The steepness of the protection curve must sometimes be bounded to achieve convergence and methods for doing so are outlined. Criteria for evaluating the fit of models are proposed and approaches to assessing the utility of results suggested. Models are evaluated by application to sixteen datasets from vaccine clinical trials. Alternative protection curve functions improved model evaluation criteria for every dataset. Standard errors based on the observed information were found to be unreliable; bootstrap estimates of precision were to be preferred. In most instances, case-cohort designs resulted in little loss of precision. Some results achieved suggested measures for utility. The original scaled logit model can be improved upon. Evaluation criteria permit well-fitting models and useful results to be identified. The proposed methods provide a comprehensive set of tools for quantifying the relationship between immunological assays and protection from disease.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 17%
Researcher 1 17%
Unknown 4 67%
Readers by discipline Count As %
Mathematics 1 17%
Medicine and Dentistry 1 17%
Unknown 4 67%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 January 2016.
All research outputs
#5,159,771
of 6,917,999 outputs
Outputs from BMC Medical Research Methodology
#603
of 738 outputs
Outputs of similar age
#206,661
of 302,338 outputs
Outputs of similar age from BMC Medical Research Methodology
#17
of 21 outputs
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