snsp1m {SenSpe}

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)