Correcting prevalence estimation for biased sampling with testing errors
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Abstract
Sampling for prevalence estimation of infection is subject to bias by both oversampling of symptomatic individuals and error‐prone tests. This results in naïve estimators of prevalence (ie, proportion of observed infected individuals in the sample) that can be very far from the true proportion of infected. In this work, we present a method of prevalence estimation that reduces both the effect of bias due to testing errors and oversampling of symptomatic individuals, eliminating it altogether in some scenarios. Moreover, this procedure considers stratified errors in which tests have different error rate profiles for symptomatic and asymptomatic individuals. This results in easily implementable algorithms, for which code is provided, that produce better prevalence estimates than other methods (in terms of reducing and/or removing bias), as demonstrated by formal results, simulations, and on COVID‐19 data from the Israeli Ministry of Health.
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SciScore for 10.1101/2021.11.12.21266254: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are a couple of limitations to our study. First, we have assumed throughout that error rates for tests are known a priori. If this is not the case, then at least under the random sampling situation, prevalence can still be estimated using a Bayesian approach described by Diggle.[12] This naturally results in increased variability of the prevalence estimate and relies on a reasonable prior distribution being elicited for the …
SciScore for 10.1101/2021.11.12.21266254: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are a couple of limitations to our study. First, we have assumed throughout that error rates for tests are known a priori. If this is not the case, then at least under the random sampling situation, prevalence can still be estimated using a Bayesian approach described by Diggle.[12] This naturally results in increased variability of the prevalence estimate and relies on a reasonable prior distribution being elicited for the prevalence. This approach has not been extended to our situation here where sampling bias is also an issue. Second, we assumed that the symptomatic without the disease group is negligible and size of sampled symptomatic individuals is at least half the population value. As for the former, we expect this to not be violated for COVID-19. Our correction proves to be very effective in many situations that would be encountered in practice. As we argued in the Introduction, this re-opens the debate about the utility of widespread rapid testing as a surveillance tool particularly in third world countries where PCR testing may be too expensive to implement widely. The scenario(s) where the correction does not improve upon the naïve estimate are those where the error rates are so large relative to the information in the sample that the correction is blurred and appears negligible. Sample pooling has also been proposed as an efficient way to estimate population prevalence because if the disease prevalence is low, then little information is accrued from individ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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