epiR (version 2.0.75)

epi.ssdxsesp: Sample size to estimate the sensitivity or specificity of a diagnostic test

Description

Sample size to estimate the sensitivity or specificity of a diagnostic test.

Usage

epi.ssdxsesp(test, type = "se", Py, epsilon, error = "relative", 
   nfractional = FALSE, conf.level = 0.95)

Value

Returns an integer defining the required sample size.

Arguments

test

scalar number, the prior estimate of diagnostic test performance (0 to 1).

type

character string. Options are se to estimate a sample size to determine diagnostic sensitivity and sp to estimate a sample size to determine diagnostic specificity.

Py

scalar number, an estimate of the prevalence of the outcome in the study population.

epsilon

scalar number, the maximum difference between the estimate and the unknown population value expressed in absolute or relative terms.

error

character string. Options are absolute for absolute error and relative for relative error.

nfractional

logical, return fractional sample size.

conf.level

scalar number, the level of confidence in the computed result.

References

Hajian-Tilaki K (2014). Sample size estimation in diagnostic test studies of biomedical informatics. Journal of Biomedical Informatics 48: 193 - 204. DOI: 10.1016/j.jbi.2014.02.013.

Examples

## EXAMPLE 1 (from Hajian-Tilaki 2014, p 195):
## A new diagnostic test has been developed and we'd like to conduct a study
## to determine its diagnostic sensitivity which we believe should be in the
## order of 0.80. How many subjects should be enrolled if the prevalence of 
## the disease outcome of interest is 0.10 and we'd like to be 95% confident 
## that our estimate of sensitivity is within 0.07 of the true population 
## value?

epi.ssdxsesp(test = 0.80, type = "se", Py = 0.10, epsilon = 0.07, 
   error = "absolute", nfractional = FALSE, conf.level = 0.95)

## A total of 1255 subjects need to be enrolled to meet the requirements of the 
## study.