Re: “Estimating Prevalence From the Results of a Screening Test”

Marchevsky, Naúm

doi:10.1093/oxfordjournals.aje.a112736

Cited by 2 publications

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“…Sensitivity and specificity are estimated from these validation data as the proportion of samples appropriately classified as positive and negative, respectively. These estimates of sensitivity and specificity can then be incorporated into the seroprevalence estimator, often with a method popularized by Rogan and Gladen (1978) (see also Marchevsky, 1979), to appropriately adjust the seroprevalence estimate for assay characteristics.…”

Section: Introductionmentioning

confidence: 99%

Estimating SARS-CoV-2 Seroprevalence

Rosin¹,

Shook-Sa²,

Cole³

et al. 2021

Preprint

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During the COVID-19 pandemic, governments and public health authorities have used seroprevalence studies to estimate the proportion of persons within a given population who have antibodies to SARS-CoV-2. Seroprevalence is crucial for estimating quantities such as the infection fatality ratio, proportion of asymptomatic cases, and differences in infection rates across population subgroups. However, serologic assays are prone to false positives and negatives, and non-probability sampling methods may induce selection bias. In this paper, we consider nonparametric and parametric seroprevalence estimators that address both challenges by leveraging validation data and assuming equal probabilities of sample inclusion within covariate-defined strata. Both estimators are shown to be consistent and asymptotically normal, and consistent variance estimators are derived. Simulation studies are presented comparing the finite sample performance of the estimators over a range of assay characteristics and sampling scenarios. The methods are used to estimate SARS-CoV-2 seroprevalence in asymptomatic individuals in Belgium and North Carolina.

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Section: Introductionmentioning

confidence: 99%

Estimating SARS-CoV-2 Seroprevalence

Rosin¹,

Shook-Sa²,

Cole³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Estimating SARS-CoV-2 seroprevalence

Rosin

Shook-Sa

Cole

et al. 2023

Journal of the Royal Statistical Society Series A: Statistics in Society

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Governments and public health authorities use seroprevalence studies to guide responses to the COVID-19 pandemic. Seroprevalence surveys estimate the proportion of individuals who have detectable SARS-CoV-2 antibodies. However, serologic assays are prone to misclassification error, and non-probability sampling may induce selection bias. In this paper, non-parametric and parametric seroprevalence estimators are considered that address both challenges by leveraging validation data and assuming equal probabilities of sample inclusion within covariate-defined strata. Both estimators are shown to be consistent and asymptotically normal, and consistent variance estimators are derived. Simulation studies are presented comparing the estimators over a range of scenarios. The methods are used to estimate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in New York City, Belgium, and North Carolina.

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Re: “Estimating Prevalence From the Results of a Screening Test”

Cited by 2 publications

References 0 publications

Estimating SARS-CoV-2 Seroprevalence

Estimating SARS-CoV-2 Seroprevalence

Estimating SARS-CoV-2 seroprevalence

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