Confidence Intervals for Seroprevalence

DiCiccio, Thomas J.; Ritzwoller, David M.; Romano, Joseph P.; Shaikh, Azeem M.

doi:10.1214/21-sts844

Cited by 5 publications

(8 citation statements)

References 38 publications

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“…Recent overviews of confidence interval procedures for prevalence in surveys without misclassification are provided by Dean and Pagano 5 and Franco et al 6 For simple random sample surveys with imperfect sensitivity and specificity, Lang and Reiczigel 7 proposed an approximate confidence interval that performed well in simulations. Recent work by DiCiccio et al 8 and Cai et al 9 study both valid (ie, exact) and approximate intervals. Their valid intervals use test inversion and the adjustment of Berger and Boos, 10 while their approximation intervals use the bootstrap with the test inversion approach.…”

Section: Introductionmentioning

confidence: 99%

“…However, in some simulated settings, our proposed methods are overly conservative, meaning that they demonstrate higher than nominal coverage, while competitor methods maintain closer to nominal coverage. We did not include in our simulations some recent methods that have been developed in response to the COVID‐19 pandemic 8,9,12 . The exact method of DiCiccio et al 8 would guarantee coverage, although applying it to a survey with a large number of strata would be “computationally expensive,” and it has not been applied to surveys using post‐stratification weighting.…”

Section: Introductionmentioning

confidence: 99%

“…Kalish et al 11 developed one such method that is closely related to one of the methods presented here, but that method's properties were not studied. Cai et al 9 (see also the discussion in DiCiccio et al 8 ) modify their approximation approach to allow sample weights for strata or individual specific weights. Another recent advancement is the method developed by Rosin et al 12 that makes use of asymptotic normal approximations which reduce to the Wald interval when sensitivity and specificity are perfect.…”

Section: Introductionmentioning

confidence: 99%

“…We did not include in our simulations some recent methods that have been developed in response to the COVID-19 pandemic. 8,9,12 The exact method of DiCiccio et al 8 would guarantee coverage, although applying it to a survey with a large number of strata would be "computationally expensive," and it has not been applied to surveys using post-stratification weighting. In contrast, our new method can very tractable in those situations.…”

Section: Introductionmentioning

confidence: 99%

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Confidence intervals for prevalence estimates from complex surveys with imperfect assays

Bayer

Graubard

2023

Statistics in Medicine

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There are established methods for estimating disease prevalence with associated confidence intervals for complex surveys with perfect assays, or simple random sample surveys with imperfect assays. We develop and study methods for the complicated case of complex surveys with imperfect assays. The new methods use the melding method to combine gamma intervals for directly standardized rates and established adjustments for imperfect assays by estimating sensitivity and specificity. One of the new methods appears to have at least nominal coverage in all simulated scenarios. We compare our new methods to established methods in special cases (complex surveys with perfect assays or simple surveys with imperfect assays). In some simulations, our methods appear to guarantee coverage, while competing methods have much lower than nominal coverage, especially when overall prevalence is very low. In other settings, our methods are shown to have higher than nominal coverage. We apply our method to a seroprevalence survey of SARS-CoV-2 in undiagnosed adults in the United States between May and July 2020.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Confidence intervals for prevalence estimates from complex surveys with imperfect assays

Bayer

Graubard

2023

Statistics in Medicine

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“…12 Methods to estimate SARS-CoV-2 seroprevalence that account for both the misclassification and variance associated with the serologic test performance have been developed and evaluated. 11,[13][14][15] Existing methods largely focus on the estimation from convenience samples such as those recruited from clinics, social media platforms, and shopping centers. [16][17][18][19] These designs offer advantages from the standpoint of time and cost, but introduce the potential for selection bias if factors related to study participation are associated with SARS-CoV-2 infection.…”

mentioning

confidence: 99%

Estimation of SARS-CoV-2 Seroprevalence in Central North Carolina: Accounting for Outcome Misclassification in Complex Sample Designs

Vias,

Cassidy,

Edwards

et al. 2023

Epidemiology

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Background: Population-based seroprevalence studies are crucial to understand community transmission of COVID-19 and guide responses to the pandemic. Seroprevalence is typically measured from diagnostic tests with imperfect sensitivity and specificity. Failing to account for measurement error can lead to biased estimates of seroprevalence. Methods to adjust seroprevalence estimates for the sensitivity and specificity of the diagnostic test have largely focused on estimation in the context of convenience sampling. Many existing methods are inappropriate when data are collected using a complex sample design. Methods: We present methods for seroprevalence point estimation and confidence interval construction that account for imperfect test performance for use with complex sample data. We apply these methods to data from the Chatham County COVID-19 Cohort (C4), a longitudinal seroprevalence study conducted in central North Carolina. Using simulations, we evaluate bias and confidence interval coverage for the proposed estimator compared with a standard estimator under a stratified, three-stage cluster sample design. Results: We obtained estimates of seroprevalence and corresponding confidence intervals for the C4 study. SARS-CoV-2 seroprevalence increased rapidly from 10.4% in January to 95.6% in July 2021 in Chatham County, North Carolina. In simulation, the proposed estimator demonstrates desirable confidence interval coverage and minimal bias under a wide range of scenarios. Conclusion: We propose a straightforward method for producing valid estimates and confidence intervals when data are based on a complex sample design. The method can be applied to estimate the prevalence of other infections when estimates of test sensitivity and specificity are available.

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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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Confidence Intervals for Seroprevalence

Cited by 5 publications

References 38 publications

Confidence intervals for prevalence estimates from complex surveys with imperfect assays

Confidence intervals for prevalence estimates from complex surveys with imperfect assays

Estimation of SARS-CoV-2 Seroprevalence in Central North Carolina: Accounting for Outcome Misclassification in Complex Sample Designs

Estimating SARS-CoV-2 seroprevalence

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