ebdt_prev: Calculate Prevalence (only Cross-sectional study)

In ebdt: Evaluation of Binary Diagnostic Test

Calculate Prevalence (only Cross-sectional study)

Description

This function calculate prevalence estimator, their standard error estimated and a confidence interval in a traverse study.

Usage

ebdt_prev(s1, r1, s0, r0, conflev = 0.95, digits = 3, verbose = TRUE)

Arguments

s1	Non-negative numeric. TP - True positive (cases correctly classified as +).
r1	Non-negative numeric. FP - False positives (controls classified as +).
s0	Non-negative numeric. FN - False negatives (cases classified as -).
r0	Non-negative numeric. TN - True negatives (controls classified as -).
conflev	Confidence level (0,1). Default 0.95.
digits	Integer. Number of decimal places. Default 3.
verbose	Logical. If TRUE, it prints the execution time. Default is TRUE.

Details

Evaluating of Binary Diagnostic Test (EBDT)

This function calculates the Prevalence (proportion of cases), standard error & Agresti-Coull CI

Value

list with: - Prevalence: prevalence estimation prev = (s1 + s0)/(s1 + s0 + r1 + r0) - StdError: binomial standard error of prevalence - CI: vector c(inf, sup) IC for prevalence - CI_Method: "Agresti-Coull"

References

Agresti, A., (2002). Categorical Data Analysis. John Wiley and Sons, New York.

Agresti, A., Coull, B.A., (1998). Approximate is better than ‘exact’ for interval estimation of binomial proportions. The American Statistician, 52:119 – 126.

Montero-Alonso, M.Á.(2010). Intervalos de confianza y contrastes de hipótesis para parámetros de tests diagnósticos binarios, http://hdl.handle.net/10481/4879

Pepe, M. S. (2003). The statistical evaluation of medical tests for classification and prediction. Oxford University Press.

Zhou, X.-H., Obuchowski, N. A., y McClish, D. K. (2011). Statistical Methods in Diagnostic Medicine (2.ª ed.). John Wiley & Sons.

Examples

ebdt_prev(40, 5, 10, 45)

ebdt documentation built on Aug. 27, 2026, 1:08 a.m.