caROC (version 0.1.5)

sscaROC_CB: Get confidence band for covariate-adjusted ROC curve for specified sub-population.

Description

Use this function to compute the confidence band for covariate-adjusted ROC curve, with or without monotonicity respecting methods for sub-population.

Usage

sscaROC_CB(diseaseData, controlData, userFormula, mono_resp_method = "none",
target_covariates, global_ROC_controlled_by = "sensitivity", CB_alpha = 0.95,
logit_CB = FALSE, nbootstrap = 100, nbin = 100, verbose = FALSE)

Arguments

diseaseData

Data from patients including dependent (biomarker) and independent (covariates) variables.

controlData

Data from controls including dependent (biomarker) and independent (covariates) variables.

userFormula

A character string to represent the function for covariate adjustment. For example, let Y denote biomarker, Z1 and Z2 denote two covariates. Then userFormula = "Y ~ Z1 + Z2".

mono_resp_method

The method used to restore monotonicity of the ROC curve or computed sensitivity/specificity value. It should one from the following: "none", "ROC". "none" is not applying any monotonicity respecting method. "ROC" is to apply ROC-based monotonicity respecting approach. Default value is "ROC".

target_covariates

Covariates of the interested sub-population. It could be a vector, e.g. c(1, 0.5, 0.8), or a matrix, e.g. target_covariates = matrix(c(1, 0.7, 0.9, 1, 0.8, 0.8), 2, 3, byrow = TRUE)

global_ROC_controlled_by

Whether sensitivity/specificity is used to control when computing global ROC. It should one from the following: "sensitivity", "specificity". Default is "sensitivity".

CB_alpha

Percentage of confidence band. Default is 0.95.

logit_CB

Whether to use logit-transformed (then transform back) confidence band. Default is FALSE.

nbootstrap

Number of boostrap iterations. Default is 100.

nbin

Number of bins used for constructing confidence band. Default is 100.

verbose

Whether to print out messages during bootstrap. Default value is FALSE.

Value

If global ROC is controlled by sensitivity, a list will be output including the following

Sensitivity

Vector of sensitivities;

Specificity_upper

Upper confidence band for specificity estimations;

Specificity_lower

Lower confidence band for specificity estimations;

global_ROC_controlled_by

"sensitivity".

If global ROC is controlled by Specificity, a list will be output including the following

Specificity

Vector of specificity;

Sensitivity_upper

Upper confidence band for sensitivity estimations;

Sensitivity_lower

Lower confidence band for sensitivity estimations;

global_ROC_controlled_by

"specificity".

Examples

# NOT RUN {
n1 = n0 = 500

## generate data
Z_D1 <- rbinom(n1, size = 1, prob = 0.3)
Z_D2 <- rnorm(n1, 0.8, 1)
Z_C1 <- rbinom(n0, size = 1, prob = 0.7)
Z_C2 <- rnorm(n0, 0.8, 1)
Y_C_Z0 <- rnorm(n0, 0.1, 1)
Y_D_Z0 <- rnorm(n1, 1.1, 1)
Y_C_Z1 <- rnorm(n0, 0.2, 1)
Y_D_Z1 <- rnorm(n1, 0.9, 1)

M0 <- Y_C_Z0 * (Z_C1 == 0) + Y_C_Z1 * (Z_C1 == 1) + Z_C2
M1 <- Y_D_Z0 * (Z_D1 == 0) + Y_D_Z1 * (Z_D1 == 1) + 1.5 * Z_D2

diseaseData <- data.frame(M = M1, Z1 = Z_D1, Z2 = Z_D2)
controlData <- data.frame(M = M0, Z1 = Z_C1, Z2 = Z_C2)

userFormula = "M~Z1+Z2"
target_covariates = c(1, 0.7, 0.9)

# default nbootstrap is 100
# set nboostrap as 10 here to improve example speed
# }
# NOT RUN {
myROCband <- sscaROC_CB(diseaseData,
                       controlData,
                       userFormula,
                       mono_resp_method = "none",
                       target_covariates,
                       global_ROC_controlled_by = "sensitivity",
                       CB_alpha = 0.95,
                       logit_CB = FALSE,
                       nbootstrap = 10,
                       nbin = 100,
                       verbose = FALSE)
# }