Clinical Calculator 2.
Predictive Values and Likelihood Ratios

Given the prevalence of a condition within the population and the sensitivity and specificity of a test designed to indicate the presence of that condition, this page will calculate the predictive values of the test (probabilities for true positive, true negative, false positive, and false negative) and its positive and negative likelihood ratios.

To proceed, enter the known or hypothetical values of prevalence, sensitivity, and selectivity into the designated cells, then click the «Calculate» button. To perform a new calculation with a new set of values, click the «Reset» button. All values should be entered as decimal fractions.
Prevalence =




Sensitivity =
Specificity =
For any particular test result:
probability that it will be positive
probability that it will be negative
For any particular positive test result:
probability that it is a true positive
["positive predictive value"]
probability that it is a false positive
For any particular negative test result:
probability that it is a true negative
["negative predictive value"]
probability that it is a false negative
likelihood Ratios:     [definitions]
Conventional Positive
Conventional Negative
Positive [weighted for prevalence]
Negative [weighted for prevalence]
Note that conventional positive and negative
likelihood ratios can be quite misleading when
prevalence substantially differs from .50.


 
Definitions of likelihood Ratios:
Conventional Positive:
=
 		conditional probability of positive
test result if the condition is present
conditional probability of positive
test result if the condition is absent
 		=
 		sensitivity
1-specificity
Conventional Negative:
=
 		conditional probability of negative
test result if the condition is present
conditional probability of negative
test result if the condition is absent
 		=
 		1-sensitivity
specificity
Positive [weighted for prevalence]
=
 		probability that a positive
test result is a true positive
probability that a positive
test result is a false positive
 		=
 		(prevalence)(sensitivity)
(1-prevalence)(1-specificity)
Negative [weighted for prevalence]
=
 		probability of false negative result
probability of true negative result
 		=
 		(prevalence)(1-sensitivity)
(1-prevalence)(specificity)