epi.ssdxsesp {epiR} | R Documentation |
Sample size to estimate the sensitivity or specificity of a diagnostic test.
epi.ssdxsesp(test, type = "se", Py, epsilon, error = "relative", nfractional = FALSE, conf.level = 0.95)
test |
scalar number, the prior estimate of diagnostic test performance (0 to 1). |
type |
character string. Options are |
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 |
nfractional |
logical, return fractional sample size. |
conf.level |
scalar number, the level of confidence in the computed result. |
Returns an integer defining the required sample size.
The sample size calculation method implemented in this function follows the approach described by Hajian-Tilaki (2014).
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.
## 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.