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Bernhard
Bernhard
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The blog you cite speaks mostly about sensitivity and specificity, shortly going towards positive and negative predictive value, without mentioning these terms.

Shouldn't the false positive rate of PCR tests be high because a low true-positive rate in the population?

Sensitivity and Specificity are defined for a sample of true positives and true negatives, without regard to the prevalence in the population. If you want to take the prevalence in the population into account, you will use the terms and definitions of "positive predictive value" (if the test is positive what are the chances of the patient being positive) and the "negative predictive value" (if the test is negative, what are the chances of the patient being negative).

As the maker and seller of the test, you can only advertise sensitivity and specificity as you cannot nowknow, on which population a doctor will employ the test you sold them. As a doctor performing the test, you are not really interested in sensitivity or specificity but in positive and negative predictive value, which depend on the prevalence of the condition.

The formula to compute predictive values from prevalence and sensitivity/specificity is called Bayes' rule.

suggested reading

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3153801/ (Westbury CF. Bayes' rule for clinicians: an introduction. Front Psychol. 2010;1:192. Published 2010 Nov 16. doi:10.3389/fpsyg.2010.00192) https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values (Wikipedia entry to the two terms that should have been in that blog post) https://en.wikipedia.org/wiki/Sensitivity_and_specificity

The blog you cite speaks mostly about sensitivity and specificity, shortly going towards positive and negative predictive value, without mentioning these terms.

Shouldn't the false positive rate of PCR tests be high because a low true-positive rate in the population?

Sensitivity and Specificity are defined for a sample of true positives and true negatives, without regard to the prevalence in the population. If you want to take the prevalence in the population into account, you will use the terms and definitions of "positive predictive value" (if the test is positive what are the chances of the patient being positive) and the "negative predictive value" (if the test is negative, what are the chances of the patient being negative).

As the maker and seller of the test, you can only advertise sensitivity and specificity as you cannot now, on which population a doctor will employ the test you sold them. As a doctor performing the test, you are not really interested in sensitivity or specificity but in positive and negative predictive value, which depend on the prevalence of the condition.

The blog you cite speaks mostly about sensitivity and specificity, shortly going towards positive and negative predictive value, without mentioning these terms.

Shouldn't the false positive rate of PCR tests be high because a low true-positive rate in the population?

Sensitivity and Specificity are defined for a sample of true positives and true negatives, without regard to the prevalence in the population. If you want to take the prevalence in the population into account, you will use the terms and definitions of "positive predictive value" (if the test is positive what are the chances of the patient being positive) and the "negative predictive value" (if the test is negative, what are the chances of the patient being negative).

As the maker and seller of the test, you can only advertise sensitivity and specificity as you cannot know, on which population a doctor will employ the test you sold them. As a doctor performing the test, you are not really interested in sensitivity or specificity but in positive and negative predictive value, which depend on the prevalence of the condition.

The formula to compute predictive values from prevalence and sensitivity/specificity is called Bayes' rule.

suggested reading

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3153801/ (Westbury CF. Bayes' rule for clinicians: an introduction. Front Psychol. 2010;1:192. Published 2010 Nov 16. doi:10.3389/fpsyg.2010.00192) https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values (Wikipedia entry to the two terms that should have been in that blog post) https://en.wikipedia.org/wiki/Sensitivity_and_specificity

Source Link
Bernhard
Bernhard
  • 8.5k
  • 18
  • 39

The blog you cite speaks mostly about sensitivity and specificity, shortly going towards positive and negative predictive value, without mentioning these terms.

Shouldn't the false positive rate of PCR tests be high because a low true-positive rate in the population?

Sensitivity and Specificity are defined for a sample of true positives and true negatives, without regard to the prevalence in the population. If you want to take the prevalence in the population into account, you will use the terms and definitions of "positive predictive value" (if the test is positive what are the chances of the patient being positive) and the "negative predictive value" (if the test is negative, what are the chances of the patient being negative).

As the maker and seller of the test, you can only advertise sensitivity and specificity as you cannot now, on which population a doctor will employ the test you sold them. As a doctor performing the test, you are not really interested in sensitivity or specificity but in positive and negative predictive value, which depend on the prevalence of the condition.