Calculates the Negative Predictive Value (only Cross-sectional study)

This function calculate the Negative predictive value estimator, their standard error estimated and a confidence interval in a traverse.

Usage

ebdt_npv(s1, r1, s0, r0, conflev = 0.95, digits = 3)

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.

Value

list with: - est: NPV = r0 / (s0 + r0) - StdError: binomial standard error of NPV - CI: vector c(inf, sup) IC for NPV - CI_Method: "Agresti-Coull"

Details

Evaluating of Binary Diagnostic Test (EBDT)

This function calculates the Negative Predictive Value (NPV) with Simel and Gart - Nam ICs

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_npv(40, 5, 10, 45)     # NPV = 45/(10+45) = 0.8182
#> 
#>  NEGATIVE PREDICTIVE VALUE 
#> ---------------------------
#> 
#> Negative Predictive Value estimated is: 0.818 
#> Standard error estimated is: 0.052 
#> Agresti-Coull Method for 95 %CI for NPV is [ 0.695 ; 0.9 ]
#>