## -----------------------------------------------------------------------------
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)))