min_possible_prevalence {BayesianReasoning}R Documentation

Show minimum possible prevalence given the test characteristics

Description

Given a FP and a desired PPV, what is the Minimum Prevalence of a Condition

Usage

min_possible_prevalence(Sensitivity = 95, FP_test = 1, min_PPV_desired = 90)

Arguments

Sensitivity

Sensitivity of the test: [0-100]

FP_test

False positive rate (1-Specificity): [0-100]

min_PPV_desired

Which PPV is what you consider the minimum to trust a positive result in the test: [0-100]

Value

A description showing the minimum necessary prevalence.

Examples


# Example 1
min_possible_prevalence(Sensitivity = 99.9, FP_test = .1, min_PPV_desired = 70)
"To reach a PPV of 70 when using a test with 99.9 % Sensitivity and 0.1 % False Positive Rate, 
you need a prevalence of at least 1 out of 429"

# Example 2
min_possible_prevalence(100, 0.1, 98)
"To reach a PPV of 98 when using a test with 100 % Sensitivity and 0.1 % False Positive Rate, 
you need a prevalence of at least 1 out of 21"

[Package BayesianReasoning version 0.3.3 Index]