Calculate Specificity — ebdt

This function calculate the specificity estimator, their standard error estimated and a confidence interval in a traverse or Cross-sectional study.

Usage

ebdt_sp(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: - Specificity: Specificity estimation (Sp = r0/(r1+r0)) - StdError: binomial standard error of Sp - CI: vector c(inf, sup) IC for Sp - CI_Method: "Agresti-Coull

Details

Evaluating of Binary Diagnostic Test (EBDT)

This function calculates the Specificity, standard error & Agresti-Coull CI

- Apply continuity correction (Haldane–Anscombe) *in pairs* if there are zeros: (r1,r0) y/o (s1,s0), +0.5 is added to both cells of the pair. - Agresti-Coull: n_tilde = n + z^2 p_tilde = (x + z^2/2)/n_tilde half = z * sqrt( p_tilde(1-p_tilde) / n_tilde )

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_sp(40, 5, 10, 45)  # Sp ≈ 0.90 with IC (Agresti-Coull)
#> 
#>  S P E C I F I C I T Y 
#> -----------------------
#> 
#> Specificity estimated is: 0.9 
#> Standard error estimated is: 0.042 
#> Agresti-Coull Method for 95 %CI for specificity is [ 0.782 ; 0.961 ]
#>