SenSpe (version 1.3)

snsp2mup: Two-biomarker unpaired comparison in specificity (or sensitivity) at a controlled sensitivity (or specificity) level

Description

Point estimation and exact bootstrap-based inference

Usage

snsp2mup(mkx, n1x, mky, n1y, s0, covp=0.95, fixsens=TRUE, lbmdisx=TRUE, lbmdisy=TRUE)

Value

diff

diff[1]: difference of empirical point estimates; diff[2]: difference of oscillating bias-corrected estimates.

hvar

exact bootstrap variance estimate for diff[1].

wald_ci

wald_ci[1,]: Wald confidence interval using diff[1]; wald_ci[2,]: Wald confidence interval using diff[2].

pct_ci

percentile confidence interval.

scr_ci

scr_ci[1,]: score confidence interval using diff[1]; scr_ci[2,]: score confidence interval using diff[2].

zq_ci

extension of the BTII in Zhou and Qin (2005, Statistics in Medicine 24, pp 465--477).

Arguments

mkx

values of biomarker X, cases followed by controls.

n1x

case size of biomarker X.

mky

values of biomarker Y, cases followed by controls.

n1y

case size of biomarker Y.

s0

controlled level of sensitivity or specificity.

covp

norminal level of confidence intervals.

fixsens

fixing sensitivity if True, and specificity otherwise.

lbmdisx

larger value of biomarker X is more associated with cases if True, and controls otherwise.

lbmdisy

larger value of biomarker Y is more associated with cases if True, and controls otherwise.

Author

Yijian Huang

References

Huang, Y., Parakati, I., Patil, D. H.,and Sanda, M. G. (2023). Interval estimation for operating characteristic of continuous biomarkers with controlled sensitivity or specificity, Statistica Sinica 33, 193--214.

Examples

set.seed(1234)
## simulate biomarker X with 100 cases and 100 controls
mkx <- c(rnorm(100,2,1),rnorm(100,0,1))
## simulate biomarker Y with 100 cases and 100 controls
mky <- c(rnorm(100,1,1),rnorm(100,0,1))

## compare specificity at controlled 0.95 sensitivity
est <- snsp2mup(mkx, 100, mky, 100, 0.95)