plotROCcurveTri: Plotting of ROC Curves for Trichotomous Biomarkers

In CoRpower: Power Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials

Description

Plots the receiver operating characteristic (ROC) curve displaying sensitivity and specificity for a range of P2 and P0 values, four values of rho, and four values of Plat2. Illustrates how different levels of measurement error rho map to sensitivity and specificity, depending on the value of Plat2. This funciton is used to create Figure 1 in the Supplementary Material of [Gilbert, Janes, and Huang (2016). "Power/Sample Size Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials."]

Usage

plotROCcurveTri(Plat0, Plat2, P0, P2, rho)

Arguments

Plat0	a numeric value specifying the prevalence of the latent lower protected subgroup for a dichotomous or trichotomous biomarker
Plat2	a numeric vector of length four specifying the prevalences of the latent higher protected subgroup for a dichotomous or trichotomous biomarker
P0	a numeric vector specifying a grid of probabilities of low biomarker response for a dichotomous or trichotomous biomarker.
P2	a numeric vector specifying a grid of probabilities of high biomarker response for a dichotomous or trichotomous biomarker.
rho	a numeric vector of length four specifying distinct protection-relevant fractions of sigma2obs.

Value

None. The function is called solely for plot generation.

Examples

Plat0 <- 0.2
Plat2 <- c(0.2, 0.3, 0.4, 0.5)
P0 <- seq(0.90, 0.10, len=10)
P2 <- seq(0.10, 0.90, len=10)
rho <- c(1, 0.9, 0.7, 0.5)
plotROCcurveTri(Plat0 = Plat0, Plat2 = Plat2, P0 = P0, P2 = P2, rho = rho)

CoRpower documentation built on Nov. 17, 2020, 9:08 a.m.