snsp1m | R Documentation |
Point estimation and exact bootstrap-based inference
snsp1m(mk, n1, s0, covp=0.95, fixsens=TRUE, lbmdis=TRUE)
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. |
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). |
Yijian Huang
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.
## 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)
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