cloppearSnSp: Binomial confidence interval, Clopper-Pearson method.

In ABS-dev/DiagTestKit: Functions used in evaluating sensitivity and specificity at CVB Statistics

Binomial confidence interval, Clopper-Pearson method.

Description

Evaluate binomial confidence interval using Clopper-Pearson method. A function written by CVB Statistics to estimate the sensitivity and specificity of an experimental diagnostic test kit in accordance with CVB STATWI0002.

Usage

cloppearSnSp(dat, alpha = 0.05, est.Sn = TRUE)

Arguments

dat	data.frame A data frame with a column for the experimental test results, a column for the infallible reference test results, and a column for the corresponding count. The column name for the experimental test results must contatin "exp" and the column name for the infallible reference test results must include "ref". The counts should be the last column.
alpha	Complement of confidence level.
est.Sn	logical (TRUE/FALSE) Indicating if the sensitivity and its confidence interval should be supplied (TRUE) or if the specificity and its confidence interval should be supplied (FALSE).

Value

An object of type cp that extends list.

• calcVal: Named vector of point estimates and estimated simulated intervals. See below.

• data: Test and Total values of the data. See below.

• alpha: Complement of the confidence interval as provided above.

If est.Sn == TRUE

calcVal is a list with the following elements

• Sn: Sensitivity estimate.

• Sn.LL: Lower confidence limit for sensitivity.

• Sn.UL: Upper confidence limit for sensitivity.

data is a list with the following elements

• Test.Positive: Number of experimental test positives.

• Total.Positive: Total number of positive samples.

If st.Sn == FALSE

calcVal is a list with the following elements

• Sp: Specificity estimate.

• Sp.LL: Lower confidence limit for specificity.

• Sp.UL: Upper confidence limit for specificity.

data is a list with the following elements

• Test.Negative: Number of experimental test negatives.

• Total.Negative: Total number of negative samples.

A matrix with a single row. If est.Sn = TRUE the columns correspond to the number of experimental test positives, the total number of positive samples, sensitivity, the lower confidence limit for sensitivity and the upper confidence limit for sensitivity. If est.Sn = FALSE, the columns correspond to the number of experimental test negatives, the total number of negative samples, specificity, the lower confidence limit for specificity and the upper confidence limit for specificity.

Author(s)

DiagTestKit-package

References

Clopper CJ, Pearson ES, 1934. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26:404-413.

Examples

CP.Sn <- cloppearSnSp(dat = dat_infal, est.Sn = TRUE)
CP.Sn
# Sn = P(T+|D+): 0.987013 (95% CI: 0.953876, 0.998423)
CP.Sp <- cloppearSnSp(dat = dat_infal, est.Sn = FALSE)
CP.Sp
# Sp = P(T-|D-): 0.970297 (95% CI: 0.915643, 0.915643)

ABS-dev/DiagTestKit documentation built on Sept. 23, 2024, 9:37 a.m.