MedDiagn: Computes the posterior probability of having a certain...

In evidence: Analysis of Scientific Evidence Using Bayesian and Likelihood Methods

Description

If experimental data on the sensitivity and the specificity of a diagnostic test are available, and the prevalence of the the condition is known with its raw data, then this function estimates the posterior probability of having the condition, with its 95% credible interval.

Usage

MedDiagn(x0, n0, x1, n1, x2, n2, N = 10000,
  alpha = 0.05, pdf = FALSE)

Arguments

x0	prevalence raw data: number of people with a certain condition
n0	number of people examined for that condition
x1	sensitivity data: number of people with the disease for whom this test was positive
n1	total number of people in the sensitivity sample
x2	specificity raw data: number of people who did not have the disease who tested negative
n2	total number of people in the specificity sample
N	number of cases to be simulated (best left at 10000 or greater
alpha	credibility required (default 95%)
pdf	set this to TRUE only if you want to keep a pdf-file of the posterior probability plot

Value

none returned: a plot and printed information are produced

Author(s)

Robert van Hulst

References

van Hulst, R. 2018. Evaluating Scientific Evidence. ms.

Examples

MedDiagn(105, 35000, 72, 80, 640, 800)

evidence documentation built on May 2, 2019, 2:14 p.m.