Preventive vaccines are an effective public health intervention for reducing the burden of infectious diseases, but have yet to be developed for several major infectious diseases. Vaccine sieve analysis studies whether and how the efficacy of a vaccine varies with the genetics of the infectious pathogen, which may help guide future vaccine development and deployment. A standard statistical approach to sieve analysis compares the effect of the vaccine to prevent infection and disease caused by pathogen types defined dichotomously as genetically near or far from a reference pathogen strain inside the vaccine construct. For example, near may be defined by amino acid identity at all amino acid positions considered in a multiple alignment and far defined by at least one amino acid difference. An alternative approach is to study the efficacy of the vaccine as a function of genetic distance from a pathogen to a reference vaccine strain where the distance cumulates over the set of amino acid positions. We propose a nonparametric method for estimating and testing the trend in the effect of a vaccine across genetic distance. We illustrate the operating characteristics of the estimator via simulation and apply the method to a recent preventive malaria vaccine efficacy trial.

Keywords: vaccines, competing risks, causal inference, marginal structural model, Hamming distance

^{1}; Juraska, Michal

^{2}; Gilbert, Peter B.

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@article{JSFS_2020__161_1_164_0, author = {Benkeser, David and Juraska, Michal and Gilbert, Peter B.}, title = {Assessing trends in vaccine efficacy by pathogen genetic distance}, journal = {Journal de la soci\'et\'e fran\c{c}aise de statistique}, pages = {164--175}, publisher = {Soci\'et\'e fran\c{c}aise de statistique}, volume = {161}, number = {1}, year = {2020}, mrnumber = {4125253}, zbl = {1443.62376}, language = {en}, url = {http://www.numdam.org/item/JSFS_2020__161_1_164_0/} }

TY - JOUR AU - Benkeser, David AU - Juraska, Michal AU - Gilbert, Peter B. TI - Assessing trends in vaccine efficacy by pathogen genetic distance JO - Journal de la société française de statistique PY - 2020 SP - 164 EP - 175 VL - 161 IS - 1 PB - Société française de statistique UR - http://www.numdam.org/item/JSFS_2020__161_1_164_0/ LA - en ID - JSFS_2020__161_1_164_0 ER -

%0 Journal Article %A Benkeser, David %A Juraska, Michal %A Gilbert, Peter B. %T Assessing trends in vaccine efficacy by pathogen genetic distance %J Journal de la société française de statistique %D 2020 %P 164-175 %V 161 %N 1 %I Société française de statistique %U http://www.numdam.org/item/JSFS_2020__161_1_164_0/ %G en %F JSFS_2020__161_1_164_0

Benkeser, David; Juraska, Michal; Gilbert, Peter B. Assessing trends in vaccine efficacy by pathogen genetic distance. Journal de la société française de statistique, Volume 161 (2020) no. 1, pp. 164-175. http://www.numdam.org/item/JSFS_2020__161_1_164_0/

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