plotVElatCont: Plotting Treatment (Vaccine) Efficacy Curves for Different Correlate of Risk Relative Risks for Continuous Biomarkers

Description

Plots the treatment (vaccine) efficacy curve for the true latent biomarker for eight different values of the latent correlate of risk relative risk and the lowest vaccine efficacy level for the true biomarker. All curves assume rho=1, and treatment (vaccine) efficacy ranges from 0 to 1. The legend is completely determined by the function.

Usage

plotVElatCont(outComputePower, outDir = NULL)

Arguments

outComputePower

a list of lists of length 1 containing output from computePower or a character string specifying the .RData file containing computePower output

outDir

a character string specifying path to output .RData file, necessary if outComputePower is a character string. Default is NULL.

Value

None. The function is called solely for plot generation.

Details

computePower function input parameter VElowest must have length greater than or equal to eight for all eight scenarios to have unique RRc and VElowest. Otherwise, only length(VElowest) unique VE curves will be displayed.

When interpreting the output of the function, the null hypothesis corresponds to a flat curve where vaccine efficacy for all values of the true latent biomarker is equal to the overall vaccine efficacy. Increasing departures from the null hypothesis correspond to increasingly variable and steep VE curves. The output assumes the overall placebo-group endpoint risk between \(\tau\) and \(\tau_{max}\) is constant for all values of the latent and observed biomarker and that there is no measurement error (\(\rho=1\)). When this is the case, an association of the biomarker with infection risk in the vaccine group (a correlate of risk) is equivalent to an association of the biomarker with treatment (vaccine) efficacy.

The function's plot can also be interpreted in conjunction with the output of the plotPowerCont function by matching the CoR relative risk in the two plots and examining power compared to VE. This sheds light on the importance of overall VE on power and further enables correlates of risk results to be interpreted in terms of potential correlates of efficacy/protection.

Examples

Run this code

# NOT RUN {
# Example scenario with continuous biomarker, where values of rho are varied

# Set input parameters for computePower function
nCasesTx <- 10
nControlsTx <- 300
nCasesTxWithS <- 10
controlCaseRatio <- 3
VEoverall <- 0.75
risk0 <- 0.034
PlatVElowest <- 0.2
VElowest <- seq(0, VEoverall, len=8)
Plat0 <- P0 <- 0.2
Plat2 <- P2 <- 0.6
M <- 13
alpha <- 0.05
sigma2obs <- 1
rho <- 1
biomType <- "continuous"

# Output from computePower function is stored in an object as a list
pwr <- computePower(nCasesTx=nCasesTx, nControlsTx=nControlsTx, nCasesTxWithS=nCasesTxWithS,
                    controlCaseRatio=controlCaseRatio, risk0=risk0, VEoverall=VEoverall,
                    PlatVElowest=PlatVElowest, VElowest=VElowest, Plat0=Plat0, Plat2=Plat2,
                    P0=P0, P2=P2, M=M, alpha=alpha, sigma2obs=sigma2obs, rho=rho, biomType=biomType)

# Set parameters for plotPowerCont function
# outComputePower is a list containing output from the computePower function
outComputePower <- pwr
plotVElatCont(outComputePower=outComputePower)

# }
# NOT RUN {
# Output from computePower function is saved in an RData file
computePower(..., saveDir = "myDir", saveFile = "myFile.RData")
# outComputePower is a character string specifying the file containing the computePower output
# outDir is a character string specifying the outComputePower file directory
outComputePower <- "myFile.RData"
outDir <- "~/myDir"
plotVElatCont(outComputePower, outDir=outDir)
# }
# NOT RUN {
# }