## ----------------------------------------------------------------------------- library(CoRpower) computeN(Nrand = 4100, # participants randomized to vaccine arm tau = 3.5, # biomarker sampling timepoint taumax = 24, # end of follow-up VEtauToTaumax = 0.75, # VE between 'tau' and 'taumax' VE0toTau = 0.75/2, # VE between 0 and 'tau' risk0 = 0.034, # placebo-group endpoint risk between 'tau' and 'taumax' dropoutRisk = 0.1, # dropout risk between 0 and 'taumax' propCasesWithS = 1) # proportion of observed cases with measured S(1) ## ----eval=FALSE--------------------------------------------------------------- # pwr <- computePower(nCasesTx = 32, # nControlsTx = 3654, # nCasesTxWithS = 32, # controlCaseRatio = c(5, 3, 1), # n^S_controls : n^S_cases ratio # VEoverall = 0.75, # overall VE # risk0 = 0.034, # placebo-group endpoint risk from tau - taumax # VElat0 = seq(0, VEoverall, len=100), # grid of VE (V/P) among lower protected # VElat1 = rep(VEoverall, 100), # grid of VE (V/P) among medium protected # Plat0 = 0.2, # prevalence of lower protected # Plat2 = 0.6, # prevalence of higher protected # P0 = 0.2, # probability of low biomarker response # P2 = 0.6, # probability of high biomarker response # sens = 0.8, spec = 0.8, FP0 = 0, FN2 = 0, # M = 1000, # number of simulated clinical trials # alpha = 0.05, # two-sided Wald test Type 1 error rate # biomType = "trichotomous") # "continuous" by default ## ----eval=FALSE--------------------------------------------------------------- # plotPowerTri(outComputePower = pwr, # 'computePower' output list of lists # legendText = paste0("Control:Case = ", c("5:1", "3:1", "1:1"))) ## ----eval=FALSE--------------------------------------------------------------- # computePower(..., saveDir = "myDir", saveFile = c("myFile1.RData", "myFile2.RData", "myFile3.RData")) # plotPowerTri(outComputePower = c("myFile1.RData", "myFile2.RData", "myFile3.RData"), # 'computePower' output files # outDir = rep("~/myDir", 3), # path to each myFilex.RData # legendText = paste0("Control:Case = ", c("5:1", "3:1", "1:1"))) ## ----eval=FALSE--------------------------------------------------------------- # pwr <- computePower(nCasesTx = 32, # nControlsTx = 3654, # nCasesTxWithS = 32, # controlCaseRatio = 5, # n^S_controls : n^S_cases ratio # VEoverall = 0.75, # overall VE # risk0 = 0.034, # placebo-group endpoint risk from tau - taumax # VElat0 = seq(0, VEoverall, len=100), # grid of VE (V/P) among lower protected # VElat1 = rep(VEoverall, 100), # grid of VE (V/P) among medium protected # Plat0 = 0.2, # prevalence of lower protected # Plat2 = 0.6, # prevalence of higher protected # P0 = 0.2, # probability of low biomarker response # P2 = 0.6, # probability of high biomarker response # sens = c(1, 0.9, 0.8, 0.7), spec = c(1, 0.9, 0.8, 0.7), # FP0 = c(0, 0, 0, 0), FN2 = c(0, 0, 0, 0), # M = 1000, # number of simulated clinical trials # alpha = 0.05, # two-sided Wald test Type 1 error rate # biomType = "trichotomous") # "continuous" by default ## ----eval=FALSE--------------------------------------------------------------- # plotPowerTri(outComputePower = pwr, # legendText = paste0("Sens = Spec = ", c(1, 0.9, 0.8, 0.7))) ## ----eval=FALSE--------------------------------------------------------------- # pwr <- computePower(nCasesTx = 32, # nControlsTx = 3654, # nCasesTxWithS = 32, # controlCaseRatio = 5, # n^S_controls : n^S_cases ratio # VEoverall = 0.75, # overall VE # risk0 = 0.034, # placebo-group endpoint risk from tau - taumax # VElat0 = seq(0, VEoverall, len=100), # grid of VE (V/P) among lower protected # VElat1 = rep(VEoverall, 100), # grid of VE (V/P) among medium protected # Plat0 = c(0.05, 0.1, 0.15, 0.2), # Plat2 = c(0.15, 0.3, 0.45, 0.6), # P0 = c(0.05, 0.1, 0.15, 0.2), # P2 = c(0.15, 0.3, 0.45, 0.6), # sens = 0.8, spec = 0.8, FP0 = 0, FN2 = 0, # M = 1000, # number of simulated clinical trials # alpha = 0.05, # two-sided Wald test Type 1 error rate # biomType = "trichotomous") # "continuous" by default ## ----eval=FALSE--------------------------------------------------------------- # plotPowerTri(outComputePower = pwr, # legendText = c("Plat0=0.05, Plat2=0.15", # "Plat0=0.1, Plat2=0.3", # "Plat0=0.15, Plat2=0.45", # "Plat0=0.2, Plat2=0.6")) ## ----eval=FALSE--------------------------------------------------------------- # pwr <- computePower(nCasesTx = 32, # nControlsTx = 3654, # nCasesTxWithS = 32, # controlCaseRatio = 5, # n^S_controls : n^S_cases ratio # VEoverall = 0.75, # overall VE # risk0 = 0.034, # placebo-group endpoint risk from tau - taumax # VElat0 = seq(0, VEoverall, len=100), # grid of VE (V/P) among lower protected # VElat1 = rep(VEoverall, 100), # grid of VE (V/P) among medium protected # Plat0 = 0.2, # prevalence of lower protected # Plat2 = 0.6, # prevalence of higher protected # P0 = 0.2, # probability of low biomarker response # P2 = 0.6, # probability of high biomarker response # sigma2obs = 1, # variance of observed biomarker S(1) # rho = c(1, 0.9, 0.7, 0.5), # protection-relevant fraction of variance of S(1) # M = 1000, # number of simulated clinical trials # alpha = 0.05, # two-sided Wald test Type 1 error rate # biomType = "trichotomous") # "continuous" by default ## ----eval=FALSE--------------------------------------------------------------- # plotPowerTri(outComputePower = pwr, # legendText = paste0("rho = ", c(1, 0.9, 0.7, 0.5))) ## ----eval=FALSE--------------------------------------------------------------- # plotRRgradVE(outComputePower = pwr, # 'computePower' output list of lists # legendText = paste0("rho = ", c(1, 0.9, 0.7, 0.5))) ## ----eval=FALSE--------------------------------------------------------------- # computePower(..., saveDir = "myDir", saveFile = "myFile.RData") # plotRRgradVE(outComputePower = paste0("myFile_rho_", c(1, 0.9, 0.7, 0.5), ".RData"), # files with 'computePower' output # outDir = "~/myDir", # path to myFile.RData # legendText = paste0("rho = ", c(1, 0.9, 0.7, 0.5))) ## ----eval=FALSE--------------------------------------------------------------- # plotROCcurveTri(Plat0 = 0.2, # Plat2 = c(0.2, 0.3, 0.4, 0.5), # P0 = seq(0.90, 0.10, len=25), # P2 = seq(0.10, 0.90, len=25), # rho = c(1, 0.9, 0.7, 0.5)) ## ----eval=FALSE--------------------------------------------------------------- # pwr <- computePower(nCasesTx = 32, # nControlsTx = 3654, # nCasesTxWithS = 32, # controlCaseRatio = 5, # n^S_controls : n^S_cases ratio # VEoverall = 0.75, # overall VE # risk0 = 0.034, # placebo-group endpoint risk from tau - taumax # VElat0 = seq(0, VEoverall, len=100), # grid of VE (V/P) among lower protected # VElat1 = rep(VEoverall, 100), # grid of VE (V/P) among medium protected # Plat0 = c(0.05, 0.1, 0.15, 0.2), # Plat2 = c(0.15, 0.3, 0.45, 0.6), # P0 = c(0.05, 0.1, 0.15, 0.2), # P2 = c(0.15, 0.3, 0.45, 0.6), # sigma2obs = 1, # variance of observed biomarker S(1) # rho = 0.9, # protection-relevant fraction of variance of S(1) # M = 1000, # number of simulated clinical trials # alpha = 0.05, # two-sided Wald test Type 1 error rate # biomType = "trichotomous") # "continuous" by default ## ----eval=FALSE--------------------------------------------------------------- # plotPowerTri(outComputePower = pwr, # legendText = c("Plat0=0.05, Plat2=0.15", # "Plat0=0.1, Plat2=0.3", # "Plat0=0.15, Plat2=0.45", # "Plat0=0.2, Plat2=0.6")) ## ----eval=FALSE--------------------------------------------------------------- # pwr <- computePower(nCasesTx = c(25, 32, 35, 40), # nControlsTx = c(3661, 3654, 3651, 3646), # nCasesTxWithS = c(25, 32, 35, 40), # controlCaseRatio = 5, # n^S_controls : n^S_cases ratio # VEoverall = 0.75, # overall VE # risk0 = 0.034, # placebo-group endpoint risk fom tau - taumax # VElat0 = seq(0, VEoverall, len=100), # grid of VE (V/P) among lower protected # VElat1 = rep(VEoverall, 100), # grid of VE (V/P) among medium protected # Plat0 = 0.2, # prevalence of lower protected # Plat2 = 0.6, # prevalence of higher protected # P0 = 0.2, # probability of low biomarker response # P2 = 0.6, # probability of high biomarker response # sigma2obs = 1, # variance of observed biomarker S(1) # rho = 0.9, # protection-relevant fraction of variance of S(1) # M = 1000, # number of simulated clinical trials # alpha = 0.05, # two-sided Wald test Type 1 error rate # biomType = "trichotomous") # "continuous" by default ## ----eval=FALSE--------------------------------------------------------------- # plotPowerTri(outComputePower = pwr, # legendText = paste0("nCasesTx = ", c(25, 32, 35, 40))) ## ----eval=FALSE--------------------------------------------------------------- # pwr <- computePower(nCasesTx = c(25, 32, 35, 40), # nControlsTx = c(3661, 3654, 3651, 3646), # nCasesTxWithS = c(25, 32, 35, 40), # controlCaseRatio = 5, # n^S_controls : n^S_cases ratio # VEoverall = 0.75, # overall VE # risk0 = 0.034, # placebo-group endpoint risk from tau - taumax # VElat0 = seq(0, VEoverall, len=100), # grid of VE (V/P) among lower protected # VElat1 = rep(VEoverall, 100), # grid of VE (V/P) among medium protected # Plat0 = 0.2, # prevalence of lower protected # Plat2 = 0.8, # prevalence of higher protected # P0 = 0.2, # probability of low biomarker response # P2 = 0.8, # probability of high biomarker response # sigma2obs = 1, # variance of observed biomarker S(1) # rho = 0.9, # protection-relevant fraction of variance of S(1) # M = 1000, # number of simulated clinical trials # alpha = 0.05, # two-sided Wald test Type 1 error rate # biomType = "binary") # "continuous" by default ## ----eval=FALSE--------------------------------------------------------------- # plotPowerTri(outComputePower = pwr, # legendText = paste0("nCasesTx = ", c(25, 32, 35, 40))) ## ----eval=FALSE--------------------------------------------------------------- # pwr <- computePower(nCasesTx = 32, # nControlsTx = 3654, # nCasesTxWithS = 32, # controlCaseRatio = 5, # n^S_controls : n^S_cases ratio # VEoverall = 0.75, # overall VE # risk0 = 0.034, # placebo-group endpoint risk from tau - taumax # PlatVElowest = 0.2, # prevalence of VE_lowest # VElowest = seq(0, VEoverall, len=100), # lowest VE for true biomarker X*<=nu # sigma2obs = 1, # variance of observed biomarker S # rho = c(1, 0.9, 0.7, 0.5) # protection-relevant fraction of variance of S # M = 1000, # number of simulated clinical trials # alpha = 0.05, # two-sided Wald test Type 1 error rate # biomType = "continuous") # "continuous" by default ## ----eval=FALSE--------------------------------------------------------------- # plotPowerCont(outComputePower = pwr, # output list of lists from 'computePower' # legendText = paste0("rho = ", c(1, 0.9, 0.7, 0.5))) ## ----eval=FALSE--------------------------------------------------------------- # computePower(..., saveDir = "myDir", saveFile = "myFile.RData") # plotPowerCont(outComputePower = paste0("myFile_rho_", c(1, 0.9, 0.7, 0.5), ".RData"), # files with 'computePower' output # outDir = "~/myDir", # path to myFile.RData # legendText = paste0("rho = ", c(1, 0.9, 0.7, 0.5))) ## ----eval=FALSE--------------------------------------------------------------- # plotVElatCont(outComputePower = pwr) ## ----eval=FALSE--------------------------------------------------------------- # computePower(..., saveDir = "myDir", saveFile = "myFile.RData") # plotVElatCont(outComputePower = "myFile.RData", # outDir = "~/myDir") ## ----eval=FALSE--------------------------------------------------------------- # pwr <- computePower(nCasesTx = 32, # nControlsTx = 3654, # nCasesTxWithS = 32, # controlCaseRatio = 5, # n^S_controls : n^S_cases ratio # VEoverall = 0.75, # overall VE # risk0 = 0.034, # placebo-group endpoint risk from tau - taumax # PlatVElowest = c(0.05, 0.1, 0.15, 0.2), # VElowest = seq(0, VEoverall, len=100), # lowest VE for true biomarker X*<=nu # sigma2obs = 1, # variance of observed biomarker S(1) # rho = 0.9 # protection-relevant fraction of variance of S(1) # M = 1000, # number of simulated clinical trials # alpha = 0.05, # two-sided Wald test Type 1 error rate # biomType = "continuous") # "continuous" by default ## ----eval=FALSE--------------------------------------------------------------- # plotPowerCont(outComputePower = pwr, # output list of lists from 'computePower' # legendText = paste0("PlatVElowest = ", c(0.05, 0.1, 0.15, 0.2))) ## ----eval=FALSE--------------------------------------------------------------- # pwr <- computePower(nCasesTx = 32, # nControlsTx = 3654, # nCasesTxWithS = 32, # cohort = TRUE, # FALSE by default # p = c(0.01, 0.02, 0.03, 0.05), # VEoverall = 0.75, # overall VE # risk0 = 0.034, # placebo-group endpoint risk from tau - taumax # VElat0 = seq(0, VEoverall, len=100), # grid of VE (V/P) among lower protected # VElat1 = rep(VEoverall, 100), # grid of VE (V/P) among medium protected # Plat0 = 0.2, # prevalence of lower protected # Plat2 = 0.6, # prevalence of higher protected # P0 = 0.2, # probability of low biomarker response # P2 = 0.6, # probability of high biomarker response # sens = 0.8, spec = 0.8, FP0 = 0, FN2 = 0, # M = 1000, # number of simulated clinical trials # alpha = 0.05, # two-sided Wald test Type 1 error rate # biomType = "trichotomous") # "continuous" by default ## ----eval=FALSE--------------------------------------------------------------- # plotPowerTri(outComputePower = pwr, # 'computePower' output # legendText = paste0("Cohort p = ", c(0.01, 0.02, 0.03, 0.05))) ## ----eval=FALSE--------------------------------------------------------------- # pwr <- computePower(nCasesTx = 32, # nControlsTx = 3654, # nCasesTxWithS = 32, # cohort = TRUE, # FALSE by default # p = c(0.01, 0.02, 0.03, 0.05), # VEoverall = 0.75, # overall VE # risk0 = 0.034, # placebo-group endpoint risk from tau - taumax # PlatVElowest = 0.2, # prevalence of VE_lowest # VElowest = seq(0, VEoverall, len=100), # lowest VE for true biomarker X*<=nu # sigma2obs = 1, # variance of observed biomarker S(1) # rho = 0.9 # protection-relevant fraction of variance of S(1) # M = 1000, # number of simulated clinical trials # alpha = 0.05, # two-sided Wald test Type 1 error rate # biomType = "continuous") # "continuous" by default ## ----eval=FALSE--------------------------------------------------------------- # plotPowerCont(outComputePower = pwr, # 'computePower' output # legendText = paste0("Cohort p = ", c(0.01, 0.02, 0.03, 0.05)))