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- W4310289042 abstract "Abstract Objectives The COVID-19 has led to many studies of seroprevalence. A number of methods exist in the statistical literature to correctly estimate disease prevalence in the presence of diagnostic test misclassification, but these methods seem to be less known and not routinely used in the public health literature. We aimed to show how widespread the problem is in recent publications, and to quantify the magnitude of bias introduced when correct methods are not used. Methods We examined a sample of recent literature to determine how often public health researcher did not account for test performance in estimates of seroprevalence. Using straightforward calculations, we estimated the amount of bias introduced when reporting the proportion of positive test results instead of using sensitivity and specificity to estimate disease prevalence. Results Of the seroprevalence studies sampled, 80% failed to account for sensitivity and specificity. Expected bias is often more than is desired in practice, ranging from 1% to 10%. Conclusions Researchers conducting studies of prevalence should correctly account for test sensitivity and specificity in their statistical analysis." @default.
- W4310289042 created "2022-11-30" @default.
- W4310289042 creator A5020220564 @default.
- W4310289042 date "2022-11-29" @default.
- W4310289042 modified "2023-09-25" @default.
- W4310289042 title "Potential for bias in (sero)prevalence estimates" @default.
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- W4310289042 doi "https://doi.org/10.1101/2022.11.24.22282720" @default.
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