Potential for bias in (sero)prevalence estimates when not accounting for test sensitivity and specificity

Haile, Sarah R.

doi:10.1101/2022.11.24.22282720

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…Uncertainty in test results due to lower sensitivity and specificity result in more noise at lower levels of prevalence [29,30]. In vaccine effectiveness studies using a test negative design this phenomenon acts to mask the effect of a vaccine in the lower prevalence vaccinated group.…”

Section: Discussionmentioning

confidence: 99%

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error

Challen,

Chatzilena,

Qian

et al. 2024

PLoS Comput Biol

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Multiplex panel tests identify many individual pathogens at once, using a set of component tests. In some panels the number of components can be large. If the panel is detecting causative pathogens for a single syndrome or disease then we might estimate the burden of that disease by combining the results of the panel, for example determining the prevalence of pneumococcal pneumonia as caused by many individual pneumococcal serotypes. When we are dealing with multiplex test panels with many components, test error in the individual components of a panel, even when present at very low levels, can cause significant overall error. Uncertainty in the sensitivity and specificity of the individual tests, and statistical fluctuations in the numbers of false positives and false negatives, will cause large uncertainty in the combined estimates of disease prevalence. In many cases this can be a source of significant bias. In this paper we develop a mathematical framework to characterise this issue, we determine expressions for the sensitivity and specificity of panel tests. In this we identify a counter-intuitive relationship between panel test sensitivity and disease prevalence that means panel tests become more sensitive as prevalence increases. We present novel statistical methods that adjust for bias and quantify uncertainty in prevalence estimates from panel tests, and use simulations to test these methods. As multiplex testing becomes more commonly used for screening in routine clinical practice, accumulation of test error due to the combination of large numbers of test results needs to be identified and corrected for.

show abstract

Section: Discussionmentioning

confidence: 99%

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error

Challen,

Chatzilena,

Qian

et al. 2024

PLoS Comput Biol

View full text Add to dashboard Cite

show abstract

“…Uncertainty in test results due to lower sensitivity and specificity result in more noise at lower levels of prevalence [27,28]. In vaccine effectiveness studies using a test negative design this phenomenon acts to mask the effect of a vaccine in the lower prevalence vaccinated group.…”

Section: Discussionmentioning

confidence: 99%

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error

Challen,

Chatzilena,

Qian

et al. 2023

Preprint

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Multiplex panel tests identify many individual pathogens at once, using a set of component tests. In some panels the number of components can be large. If the panel is detecting causative pathogens for a single syndrome or disease then we might estimate the burden of that disease by combining the results of the panel, for example determining the prevalence of pneumococcal pneumonia as caused by many individual pneumococcal serotypes. When we are dealing with multiplex test panels with many components, test error in the individual components of a panel, even when present at very low levels, can cause significant overall error. Uncertainty in the sensitivity and specificity of the individual tests, and statistical fluctuations in the numbers of false positives and false negatives, will cause large uncertainty in the combined estimates of disease prevalence. In many cases this can be a source of significant bias. In this paper we develop a mathematical framework to characterise this issue, present novel statistical methods that adjust for this bias and quantify uncertainty, and use simulation to test these methods. As multiplex testing becomes more commonly used for screening in routine clinical practice, accumulation of test error due to the combination of large numbers of test results needs to be identified and corrected for.Author summaryDuring analysis of pneumococcal incidence data obtained from serotype specific multiplex urine antigen testing, we identified that despite excellent test sensitivity and specificity, the small error rate in each individual serotype test has the potential to compound and cause large uncertainty in the resulting estimates of pneumococcal prevalence, obtained by combining individual results. This limits the accuracy of estimates of the burden of disease caused by vaccine preventable pneumococcal serotypes, and in certain situations can produce marked bias.

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Potential for bias in (sero)prevalence estimates when not accounting for test sensitivity and specificity

Cited by 2 publications

References 31 publications

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error

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