snsp2mup | R Documentation |
Point estimation and exact bootstrap-based inference
snsp2mup(mkx, n1x, mky, n1y, s0, covp=0.95, fixsens=TRUE, lbmdisx=TRUE, lbmdisy=TRUE)
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. |
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). |
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.
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)
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