snsp1m: Estimating specificity (or sensitivity) at a controlled...

In SenSpe: Estimating Specificity at Controlled Sensitivity, or Vice Versa

Estimating specificity (or sensitivity) at a controlled sensitivity (or specificity) level

Description

Point estimation and exact bootstrap-based inference

Usage

snsp1m(mk, n1, s0, covp=0.95, fixsens=TRUE, lbmdis=TRUE)

Arguments

mk	biomarker values of cases followed by controls.
n1	size of cases.
s0	controlled level of sensitivity or specificity.
covp	norminal level of confidence intervals.
fixsens	fixing sensitivity if True, and specificity otherwise.
lbmdis	larger biomarker value is more associated with cases if True, and controls otherwise.

Value

threshold	estimated threshold, at and beyond which the empirical sensitivity or specificity is the smallest no less than the controlled level s0.
hss	hss[1]: empirical point estimate of specificity at controlled sensitivity, or vice versa; hss[2]: oscillating bias-corrected estimate.
hvar1	estimated variance component from cases if specificity at controlled sensitivity is estimated, or from controls otherwise.
hvar2	estimated variance component from controls if specificity at controlled sensitivity is estimated, or from cases otherwise.
hvar	exact bootstrap variance estimate, =hvar1+hvar2.
btpdf	exact bootstrap probability mass function at (0:n0)/n0 with n0 being the size of controls if sensitivity is controlled, or at (0:n1)/n1 otherwise.
wald_ci	wald_ci[1,]: Wald confidence interval using hss[1]; wald_ci[2,]: Wald confidence interval using hss[2].
pct_ci	percentile confidence interval.
scr_ci	scr_ci[1,]: score confidence interval using hss[1]; scr_ci[2,]: score confidence interval using hss[2].
zq_ci	exact bootstrap version of the BTII in Zhou and Qin (2005, Statistics in Medicine 24, pp 465–477).

Author(s)

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

## simulate biomarkers of 100 cases and 100 controls
set.seed(1234)
mk <- c(rnorm(100,1,1),rnorm(100,0,1))

## estimate specificity at controlled 0.95 sensitivity
est <- snsp1m(mk, 100, 0.95)

SenSpe documentation built on May 29, 2024, 9:21 a.m.