# Plotting of Power versus Correlate of Risk Effect Size for Dichotomous and Trichotomous Biomarkers Plots power (on the y-axis) to detect a correlate of risk effect size (on the x-axis) in the active treatment group for a dichotomous or trichotomous biomarker. The correlate of risk effect size is quantified as the relative risk of the clinical endpoint comparing subgroups of active treatment recipients with high and low biomarker response. ```r plotPowerTri( outComputePower, outDir = NULL, legendText, legendTitle = NULL, extendedLeg = TRUE, verboseLeg = TRUE, margin = c(11, 7, 3, 1) ) ``` ## Arguments - `outComputePower`: either a list of lists containing output from `computePower` or a character vector specifying the `.RData` file(s) containing `computePower` output - `outDir`: a character vector specifying path(s) to output `.RData` file(s), necessary if `outComputePower` is a character vector. Default is `NULL`. - `legendText`: a character vector specifying the entirety of the legend text. The order of the elements (i.e., parameter values) must match that of the `computePower` input parameters in order for legend labels to be accurate. - `legendTitle`: a character vector specifying the legend title if applicable (`NULL` by default) - `extendedLeg`: a logical value specifying if the extended footnote legend with additional information about the control-to-case ratio, overall vaccine efficacy, number of cases, etc., is to be included. Default is `TRUE`. - `verboseLeg`: a logical value specifying if the extended footnote legend shall use English words (`TRUE` by default) or mathematical notation used in Gilbert, Janes, and Huang (2016) - `margin`: a numeric vector of the form `c(bottom, left, top, right)`, which specifies the margins of the plot. Default is `c(11, 7, 3, 1)`. ## Returns None. The function is called solely for plot generation. ## Details If multiple levels are specified for the biomarker measurement error input parameters (i.e., for `sens`/`spec` or `rho`) in `computePower`, only the first level is used to determine the $RR_t$ values shown as x-axis tickmark labels. ## Examples ```r # Example scenario with trichotomous biomarker, where values of controlCaseRatio are varied # Set input parameters for computePower function nCasesTx <- 10 nControlsTx <- 300 nCasesTxWithS <- 10 controlCaseRatio <- c(5,3) VEoverall <- 0.75 risk0 <- 0.034 VElat0 <- seq(0, VEoverall, len=5) VElat1 <- rep(VEoverall, 5) Plat0 <- P0 <- 0.2 Plat2 <- P2 <- 0.6 sens <- spec <- 0.8 FP0 <- FN2 <- 0 M <- 50 alpha <- 0.05 biomType <- "trichotomous" # Output from computePower function is stored in an object as a list of lists pwr <- computePower(nCasesTx=nCasesTx, nControlsTx=nControlsTx, nCasesTxWithS=nCasesTxWithS, controlCaseRatio=controlCaseRatio, risk0=risk0, VEoverall=VEoverall, Plat0=Plat0, Plat2=Plat2, P0=P0, P2=P2, VElat0=VElat0, VElat1=VElat1, M=M, alpha=alpha, spec=spec, FP0=FP0, sens=sens, FN2=FN2, biomType=biomType) # Set parameters for plotPowerTri function # outComputePower is a list of lists containing outputs from the computePower function outComputePower <- pwr legendText <- paste0("controls:cases = ", c("5:1","3:1")) plotPowerTri(outComputePower=outComputePower, legendText=legendText) ## Not run: # outComputePower is a character vector specifying the files containing computePower output # outDir is a character vector specifying the outComputePower file directories outComputePower <- paste0("myFile_controlCaseRatio_", c(5, 3), ".RData") outDir <- rep("~/myDir", 2) legendText <- paste0("controls:cases = ", c("5:1","3:1")) plotPowerTri(outComputePower, outDir=outDir, legendText = legendText) ## End(Not run) ``` ## References Gilbert P. B., Janes H., and Huang Y. (2016), Power/Sample Size Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials. **Stat Med** 35(21):3745-59. ## See Also `computePower`, `plotPowerCont`