snsp2mp | R Documentation |
Point estimation and exact bootstrap-based inference
snsp2mp(mk, n1, s0, covp=0.95, fixsens=TRUE, lbmdis=TRUE)
mk |
Each of two rows corresponds to a biomarker, cases followed by controls. |
n1 |
case size. |
s0 |
controlled level of sensitivity or specificity. |
covp |
norminal level of confidence intervals. |
fixsens |
fixing sensitivity if True, and specificity otherwise. |
lbmdis |
larger value of a biomarker is more associated with cases if True, and controls otherwise. |
diff |
diff[1]: difference of empirical point estimates; hss[2]: difference of oscillating bias-corrected estimates. |
btmn |
bootstrap mean of the empirical difference. |
btva |
exact bootstrap variance estimate for diff[1]. |
btdist |
exact bootstrap probability mass function at (-n0:n0)/n0 with n0 being the size of controls if sensitivity is controlled, or at (-n1:n1)/n1 otherwise. |
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.
## simulate paired biomarkers X and Y, with correlation 0.5, 100 cases and 100 controls
n1 <- 100
n0 <- 100
rho <- 0.5
set.seed(1234)
mkx <- rnorm(n1+n0,0,1)
mky <- rho*mkx + sqrt(1-rho^2)*rnorm(n1+n0,0,1)
mkx <- mkx + c(rep(2,n1),rep(0,n0))
mky <- mky + c(rep(1,n1),rep(0,n0))
mk <- rbind(mkx,mky)
## compare specificity at controlled 0.95 sensitivity
est <- snsp2mp(mk, 100, 0.95)
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