View source: R/PVBcorrect_functions.R
acc_bg | R Documentation |
PVB correction by Begg and Greenes' method with asymptotic normal CI. This is limited to no covariate.
acc_bg(data, test, disease, ci = FALSE, ci_level = 0.95, description = TRUE)
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 |
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 |
A list object containing:
The accuracy results.
Begg, C. B., & Greenes, R. A. (1983). Assessment of diagnostic tests when disease verification is subject to selection bias. Biometrics, 207–215.
Harel, O., & Zhou, X.-H. (2006). Multiple imputation for correcting verification bias. Statistics in Medicine, 25(22), 3769–3786.
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.
Zhou, X.-H. (1994). Effect of verification bias on positive and negative predictive values. Statistics in Medicine, 13(17), 1737–1745.
Zhou, X.-H., Obuchowski, N. A., & McClish, D. K. (2011). Statistical Methods in Diagnostic Medicine (2nd ed.). John Wiley & Sons.
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()
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