PVBcorrect (version 0.3.1)

acc_bg: PVB correction by Begg and Greenes' method with asymptotic normal CI

Description

PVB correction by Begg and Greenes' method with asymptotic normal CI. This is limited to no covariate.

Usage

acc_bg(data, test, disease, ci = FALSE, ci_level = 0.95, description = TRUE)

Value

A list object containing:

acc_results

The accuracy results.

Arguments

data

A data frame, with at least "Test" and "Disease" variables.

test

The "Test" variable name, i.e. the test result. The variable must be in binary; positive = 1, negative = 0 format.

disease

The "Disease" variable name, i.e. the true disease status. The variable must be in binary; positive = 1, negative = 0 format.

ci

View confidence interval (CI). The default is FALSE.

ci_level

Set the CI width. The default is 0.95 i.e. 95% CI.

description

Print the name of this analysis. The default is TRUE. This can be turned off for repeated analysis, for example in bootstrapped results.

References

1. Begg, C. B., & Greenes, R. A. (1983). Assessment of diagnostic tests when disease verification is subject to selection bias. Biometrics, 207–215.

2. Harel, O., & Zhou, X.-H. (2006). Multiple imputation for correcting verification bias. Statistics in Medicine, 25(22), 3769–3786.

3. Zhou, X.-H. (1993). Maximum likelihood estimators of sensitivity and specificity corrected for verification bias. Communications in Statistics-Theory and Methods, 22(11), 3177–3198.

4. Zhou, X.-H. (1994). Effect of verification bias on positive and negative predictive values. Statistics in Medicine, 13(17), 1737–1745.

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

Examples

acc_bg(data = cad_pvb, test = "T", disease = "D")  # equivalent to result by acc_ebg()
acc_bg(data = cad_pvb, test = "T", disease = "D", ci = TRUE)
  # the CIs are slightly differerent from result by acc_ebg()