Nothing
R/SimSpsoContour.R
In Phase12Compare: Simulates SPSO and Efftox Phase 12 Trials with Correlated Outcomes
Defines functions
SimSpsoContour
Documented in
SimSpsoContour
#'Simulates trial replications from the SPSO model with desirability optimality function.
#'
#'Simulates replications from a Bayesian adaptive phase 12 clinical trial design using the SPSO model and a optimality function based on the desirability tradeoff contour.
#'@param NSims Number of trial simulations to run.
#'@param PE True efficacy probability for each dose.
#'@param PT True toxicity probaiblity for each dose.
#'@param corET Correlation parameter between efficacy and toxicity probability.
#'@param Nmax Maximum trial sample size.
#'@param cohort Patient cohort size.
#'@param Contour Contour vector for desirability function. Contains in order: (pi_{1,E},pi_{2,T},pi_{3,E},pi_{4,T}).
#'@param CutE Cutoff for efficacy probability acceptability. Dose-efficacy probabilities must be larger than this for patient assignment.
#'@param CutT Cutoff for toxicity probability acceptability. Dose-toxicity probabilities must be smaller than this for patient assignment.
#'@param AcceptE Posterior probability threshold for efficacy acceptability.
#'@param AcceptT Posterior probability threshold for toxicity acceptability.
#'@param HypermeansE Hypermeans for dose-specific efficacy parameters.
#'@param HypermeansT Hypermeans for dose-specific toxcity parameters.
#'@param Hypervars Hypervariances needed for the SPSO model. Contains, in order (sigma_0^2, sigma_mu^2,tau).
#'@return A list with the first entry corresponding to a matrix with: (1) True toxicity probabilities at each dose, (2) True efficacy probabilities at each dose, (3) True Desirability of each dose, (4) Optimal dose selection probability, (5) Average sample size of patients treated at each dose. The second entry of the list contains a vector with rows corresponding to (1) the true binary bivariate correlation between efficacy and toxicity, (2) Stopping probability of the trial, (3) Average number of efficacy events, (4) Average number of toxicity events, and (5) Delta.
#'@importFrom mvtnorm rmvnorm dmvnorm pmvnorm
#'@importFrom stats rnorm runif var pnorm qnorm
#'@examples
#'library(mvtnorm)
#'##True toxicity probability
#'PT=c(.05,.10,.15,.20,.30)
#'##True Efficacy Probability
#'PE=c(.2,.4,.6,.65,.7)
#'#True Correlation
#'corET=.5
#'##Number of simulations
#'NSims=1 ##Increase this when using
#'##Hypermeans and hypervariances
#'HypermeansE=c(-1.189, -0.357, 0.360, 0.546, 0.743)
#'HypermeansT=c(-2.325, -1.811, -1.464, -1.189, -0.740)
#'Hypervars=c(1,16,1)
#'##Trial Parameters
#'##Cohort Size, N^F and N_ET
#'cohort=3
#'##Starting Dose
#'DoseStart=1
#'Nmax=30 ##Max Sample Size
#'#Acceptability Criterion
#'CutE=.3
#'CutT=.4
#'##Limits on acceptability
#'AcceptE=.1
#'AcceptT=.1
#'##Contour vector
#'Contour = c(.35, .75,.7,.4)
#'RESULTS=SimSpsoContour(NSims, PE, PT, corET, Nmax, cohort, Contour,
#'CutE, CutT, AcceptE, AcceptT,HypermeansE, HypermeansT, Hypervars)
#'RESULTS
#'@export
SimSpsoContour=function(
NSims, ##Number of simulations
PE, ##True Efficacy Probability for each dose
PT, ##True toxicity probaiblity for each dose
corET, ##Correlation parameter between Eff and Tox
Nmax, ##MAximum Sample size
cohort, ##Cohort Size
Contour, ##Contour
CutE, ##Cutoff For efficacy acceptability
CutT, ##Cutoff for toxicity acceptability
AcceptE, ##Probability threshold for eff acceptability
AcceptT, ##Probability threshold for tox acceptability
HypermeansE, ##Hypermeans for efficacy
HypermeansT, ##Hypermeans for Toxcity
Hypervars ##Hypervariances
){
NSkip=0 ##Will count the number of skipped simulations
NF=Nmax
nDose=length(HypermeansE)
NumTrt=matrix(rep(NA,nDose*NSims),nrow=NSims)
CORMAT <- matrix(c(1,corET,corET,1), ncol=2)
CORRMAT=CORMAT
Z1=CORMAT
##Calculate the True Utility Scores at each dose level
TRUEUT=rep(NA,nDose)
Sigma=matrix(c(1,corET,
corET, 1), nrow=2, byrow=TRUE)
PMAT=Sigma
MeanUT=rep(0,nDose)
Sigma=matrix(c(1,corET,
corET, 1), nrow=2, byrow=TRUE)
PMAT=Sigma
MeanUT=rep(0,nDose)
PROBS=matrix(rep(NA,4*nDose),ncol=4)
for(D in 1:nDose){
MU=c(qnorm(PE[D]),qnorm(PT[D]))
##YE,YT=0,0
lower <- rep(-Inf, 2)
upper <- rep(0, 2)
PMAT[1,1] <- pmvnorm(lower, upper, MU, sigma=Sigma)
PROBS[D,1]=PMAT[1,1]
##YE,YT=0,1
lower <- c(-Inf,0)
upper <- c(0,Inf)
PMAT[2,1] <- pmvnorm(lower, upper, MU, sigma=Sigma)
PROBS[D,3]=PMAT[2,1]
##YE,YT = 1,0
lower <- c(0,-Inf)
upper <- c(Inf,0)
PMAT[1,2] <- pmvnorm(lower, upper, MU, sigma=Sigma)
PROBS[D,2]=PMAT[1,2]
##YE, YT = 1,1
lower <- c(0,0)
upper <- c(Inf,Inf)
PMAT[2,2] <- pmvnorm(lower, upper, MU, sigma=Sigma)
PROBS[D,4]=PMAT[2,2]
}
TRUEUT=MeanUT
##Setup Simulation parameters
SIMSTORE = as.list(rep(0,NSims))
DoseOpt=rep(NA,NSims)
NTox = rep(NA,NSims)
NEff=rep(NA,NSims)
DoseStore=rep(NA,Nmax)
YEStore = rep(NA,Nmax)
YTStore =rep(NA,Nmax)
B=2000
for(m1 in 1:NSims){
if(m1%%1000==0){
cat(paste(m1,"Simulations
",sep=" "))
}
Dosetried=c(1,rep(0,nDose-1))
##Start at lowest dose
Doses=rep(1,cohort)
OUT <- GETBIN(PROBS[1,])
YE=OUT[1]
YT=OUT[2]
if(cohort>1){
for(i in 2:cohort){
OUT <- GETBIN(t(PROBS[1,]))
YE=c(YE,OUT[1])
YT=c(YT,OUT[2])
}
}
##Ok now we have the starting values
for(i in 2:(Nmax/cohort)){
Z=UTEFFTOX(YE,YT, Doses, HypermeansE, HypermeansT, Hypervars, B )
MeanUT=rep(0,nDose)
Sigma=matrix(c(Hypervars[1]+Hypervars[3],Hypervars[3]*mean(Z[[3]]),
Hypervars[3]*mean(Z[[3]]), Hypervars[1]+Hypervars[3]), nrow=2, byrow=TRUE)
PMAT=Sigma
MeanUT=rep(0,nDose)
for(D in 1:nDose){
MU=c(mean(Z[[1]][,D]),mean(Z[[2]][,D]))
PEFF = 1-pnorm(0,MU[1],sqrt(Hypervars[1]+Hypervars[3]))
PTOX = 1-pnorm(0,MU[2],sqrt(Hypervars[1]+Hypervars[3]))
MeanUT[D]=GetDesire(PEFF,PTOX,Contour)
}
ACCEPTE=rep(1,nDose)
ACCEPTT=rep(1,nDose)
##What Doses are acceptable?
for(D in 1:nDose){
##Calculate the probability of eff over all the samples
PEFF = 1-pnorm(0,Z[[1]][,D],sqrt(Hypervars[1]+Hypervars[3]))
if(sum(Doses==D)>=cohort){
##IS THIS DOSE ACCEPTABLE IN TERMS OF EFFICACY?
ACCEPTE[D]=mean(PEFF>=CutE)>AcceptE
}
##Calculate the probability of tox over all the samples
PTOX = 1-pnorm(0,Z[[2]][,D],sqrt(Hypervars[1]+Hypervars[3]))
##IS THIS DOSE ACCEPTABLE IN TERM OF TOXICITY?
ACCEPTT[D]=mean(PTOX<=CutT)>AcceptT
}
## ACCEPTE[which(sum(Doses)<cohort)]=1
if(sum(ACCEPTE*ACCEPTT)==0 && i>2){
OptDose=0
break
}else{
##Multiply MeanUT by accept
for(j in 1:nDose){
if(ACCEPTE[j]*ACCEPTT[j]==0){
MeanUT[j]=-1000
}}
##Assign Next Dose Deterministically
OptDose = ReturnOpt(MeanUT,Dosetried) +1
Dosetried[OptDose]=1
##Now enroll the next three patients
Doses=c(Doses,rep(OptDose,cohort))
#What are their binary outcomes
for(m in 1:cohort){
OUT <- GETBIN(PROBS[OptDose,])
YE=c(YE,OUT[1])
YT=c(YT,OUT[2])
}
}
}
if(OptDose==0){
NSkip=NSkip+1
##This simulation doesn't count now.
X1=as.list(c(0,0,0))
X1[[1]]=OptDose
X1[[2]]=MeanUT
X1[[3]]=cbind(Doses,YE,YT)
SIMSTORE[[m1]]=X1
NTox[m1]=sum(YT)
NEff[m1]=sum(YE)
DoseOpt[m1]=OptDose
for(j in 1:nDose){
NumTrt[m1,j]=sum(Doses==j)
}
}else{
##Ok now weve done the whole trial, whats the optimal dose
Z=UTEFFTOX(YE,YT, Doses, HypermeansE, HypermeansT, Hypervars, B )
MeanUT=rep(0,nDose)
Sigma=matrix(c(Hypervars[1]+Hypervars[3],Hypervars[3]*mean(Z[[3]]),
Hypervars[3]*mean(Z[[3]]), Hypervars[1]+Hypervars[3]), nrow=2, byrow=TRUE)
PMAT=Sigma
for(D in 1:nDose){
MU=c(mean(Z[[1]][,D]),mean(Z[[2]][,D]))
PEFF = 1-pnorm(0,MU[1],sqrt(Hypervars[1]+Hypervars[3]))
PTOX = 1-pnorm(0,MU[2],sqrt(Hypervars[1]+Hypervars[3]))
MeanUT[D]=GetDesire(PEFF,PTOX,Contour)
}
ACCEPTE=rep(1,nDose)
ACCEPTT=rep(1,nDose)
##What Doses are acceptable?
for(D in 1:nDose){
##Calculate the probability of eff over all the samples
PEFF = 1-pnorm(0,Z[[1]][,D],sqrt(Hypervars[1]+Hypervars[3]))
if(sum(Doses==D)>=cohort){
##IS THIS DOSE ACCEPTABLE IN TERMS OF EFFICACY?
ACCEPTE[D]=mean(PEFF>=CutE)>AcceptE
}
##Calculate the probability of tox over all the samples
PTOX = 1-pnorm(0,Z[[2]][,D],sqrt(Hypervars[1]+Hypervars[3]))
##IS THIS DOSE ACCEPTABLE IN TERM OF TOXICITY?
ACCEPTT[D]=mean(PTOX<=CutT)>AcceptT
}
##Multiply MeanUT by accept
if(sum(ACCEPTE*ACCEPTT)==0){
OptDose=0
}else{
for(j in 1:nDose){
if(ACCEPTE[j]*ACCEPTT[j]*Dosetried[j]==0){
MeanUT[j]=-1000
}}
##Assign Next Dose Deterministically
OptDose = which(MeanUT==max(MeanUT))
}
##Fill this list with our values
X1=as.list(c(0,0,0))
X1[[1]]=min(OptDose)
X1[[2]]=MeanUT
X1[[3]]=cbind(Doses,YE,YT)
SIMSTORE[[m1]]=X1
NTox[m1]=sum(YT)
NEff[m1]=sum(YE)
for(j in 1:nDose){
NumTrt[m1,j]=sum(Doses==j)
}
DoseOpt[m1]=min(OptDose)
}
}
##Calculate True Accept
TRUECEPT = (PE>=CutE)*(PT<=CutT)
##These are the doses that are truly acceptable
##Calculate the true desireability
##Shift Desire to 0-100 range
desire=TRUEUT
for(j in 1:length(desire)){
desire[j]=GetDesire(PE[j],PT[j],Contour)
}
##desire = 50*desire+50
desire = 100*(desire - GetDesire(0,1,Contour))/(1-GetDesire(0,1,Contour))
TRUECEPT = (PE>=CutE)*(PT<=CutT)
##These are the doses that are truly acceptable
prob1=rep(0,nDose)
for(j in 1:nDose){
prob1[j]=mean(DoseOpt==j)
}
X=rbind(1:nDose,round(PT,2),round(PE,2),TRUECEPT,round(desire,2),prob1,round(colMeans(NumTrt),2))
rownames(X)=c("Dose #","True Toxicity Probability:","True Efficacy Probability:",
"Is the dose acceptable?", "True Desirability:",
"Dose Selection Probability:", "Average # Treated:")
colnames(X)=1:nDose
Z=as.list(c(0,0))
Z[[1]]=X
##Next we need the Single values
X1=c(mean(DoseOpt==0),mean(NEff),mean(NTox))
TRUECEPT = (PE>=CutE)*(PT<=CutT)
##Utilities of ACCEPTABLE doses
U1=desire*TRUECEPT
X1=c(corET,X1,round(sum(prob1*(U1-min(U1))/(max(U1)-min(U1))),2))
X1=as.matrix(X1)
rownames(X1)=c("CorET","P[Stop]","NEff","NTox","Delta")
Z[[2]]=X1
return(Z)
}
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Defines functions SimSpsoContour
Documented in SimSpsoContour
#'Simulates trial replications from the SPSO model with desirability optimality function.
#'
#'Simulates replications from a Bayesian adaptive phase 12 clinical trial design using the SPSO model and a optimality function based on the desirability tradeoff contour.
#'@param NSims Number of trial simulations to run.
#'@param PE True efficacy probability for each dose.
#'@param PT True toxicity probaiblity for each dose.
#'@param corET Correlation parameter between efficacy and toxicity probability.
#'@param Nmax Maximum trial sample size.
#'@param cohort Patient cohort size.
#'@param Contour Contour vector for desirability function. Contains in order: (pi_{1,E},pi_{2,T},pi_{3,E},pi_{4,T}).
#'@param CutE Cutoff for efficacy probability acceptability. Dose-efficacy probabilities must be larger than this for patient assignment.
#'@param CutT Cutoff for toxicity probability acceptability. Dose-toxicity probabilities must be smaller than this for patient assignment.
#'@param AcceptE Posterior probability threshold for efficacy acceptability.
#'@param AcceptT Posterior probability threshold for toxicity acceptability.
#'@param HypermeansE Hypermeans for dose-specific efficacy parameters.
#'@param HypermeansT Hypermeans for dose-specific toxcity parameters.
#'@param Hypervars Hypervariances needed for the SPSO model. Contains, in order (sigma_0^2, sigma_mu^2,tau).
#'@return A list with the first entry corresponding to a matrix with: (1) True toxicity probabilities at each dose, (2) True efficacy probabilities at each dose, (3) True Desirability of each dose, (4) Optimal dose selection probability, (5) Average sample size of patients treated at each dose. The second entry of the list contains a vector with rows corresponding to (1) the true binary bivariate correlation between efficacy and toxicity, (2) Stopping probability of the trial, (3) Average number of efficacy events, (4) Average number of toxicity events, and (5) Delta.
#'@importFrom mvtnorm rmvnorm dmvnorm pmvnorm
#'@importFrom stats rnorm runif var pnorm qnorm
#'@examples
#'library(mvtnorm)
#'##True toxicity probability
#'PT=c(.05,.10,.15,.20,.30)
#'##True Efficacy Probability
#'PE=c(.2,.4,.6,.65,.7)
#'#True Correlation
#'corET=.5
#'##Number of simulations
#'NSims=1 ##Increase this when using
#'##Hypermeans and hypervariances
#'HypermeansE=c(-1.189, -0.357, 0.360, 0.546, 0.743)
#'HypermeansT=c(-2.325, -1.811, -1.464, -1.189, -0.740)
#'Hypervars=c(1,16,1)
#'##Trial Parameters
#'##Cohort Size, N^F and N_ET
#'cohort=3
#'##Starting Dose
#'DoseStart=1
#'Nmax=30 ##Max Sample Size
#'#Acceptability Criterion
#'CutE=.3
#'CutT=.4
#'##Limits on acceptability
#'AcceptE=.1
#'AcceptT=.1
#'##Contour vector
#'Contour = c(.35, .75,.7,.4)
#'RESULTS=SimSpsoContour(NSims, PE, PT, corET, Nmax, cohort, Contour,
#'CutE, CutT, AcceptE, AcceptT,HypermeansE, HypermeansT, Hypervars)
#'RESULTS
#'@export
SimSpsoContour=function(
NSims, ##Number of simulations
PE, ##True Efficacy Probability for each dose
PT, ##True toxicity probaiblity for each dose
corET, ##Correlation parameter between Eff and Tox
Nmax, ##MAximum Sample size
cohort, ##Cohort Size
Contour, ##Contour
CutE, ##Cutoff For efficacy acceptability
CutT, ##Cutoff for toxicity acceptability
AcceptE, ##Probability threshold for eff acceptability
AcceptT, ##Probability threshold for tox acceptability
HypermeansE, ##Hypermeans for efficacy
HypermeansT, ##Hypermeans for Toxcity
Hypervars ##Hypervariances
){
NSkip=0 ##Will count the number of skipped simulations
NF=Nmax
nDose=length(HypermeansE)
NumTrt=matrix(rep(NA,nDose*NSims),nrow=NSims)
CORMAT <- matrix(c(1,corET,corET,1), ncol=2)
CORRMAT=CORMAT
Z1=CORMAT
##Calculate the True Utility Scores at each dose level
TRUEUT=rep(NA,nDose)
Sigma=matrix(c(1,corET,
corET, 1), nrow=2, byrow=TRUE)
PMAT=Sigma
MeanUT=rep(0,nDose)
Sigma=matrix(c(1,corET,
corET, 1), nrow=2, byrow=TRUE)
PMAT=Sigma
MeanUT=rep(0,nDose)
PROBS=matrix(rep(NA,4*nDose),ncol=4)
for(D in 1:nDose){
MU=c(qnorm(PE[D]),qnorm(PT[D]))
##YE,YT=0,0
lower <- rep(-Inf, 2)
upper <- rep(0, 2)
PMAT[1,1] <- pmvnorm(lower, upper, MU, sigma=Sigma)
PROBS[D,1]=PMAT[1,1]
##YE,YT=0,1
lower <- c(-Inf,0)
upper <- c(0,Inf)
PMAT[2,1] <- pmvnorm(lower, upper, MU, sigma=Sigma)
PROBS[D,3]=PMAT[2,1]
##YE,YT = 1,0
lower <- c(0,-Inf)
upper <- c(Inf,0)
PMAT[1,2] <- pmvnorm(lower, upper, MU, sigma=Sigma)
PROBS[D,2]=PMAT[1,2]
##YE, YT = 1,1
lower <- c(0,0)
upper <- c(Inf,Inf)
PMAT[2,2] <- pmvnorm(lower, upper, MU, sigma=Sigma)
PROBS[D,4]=PMAT[2,2]
}
TRUEUT=MeanUT
##Setup Simulation parameters
SIMSTORE = as.list(rep(0,NSims))
DoseOpt=rep(NA,NSims)
NTox = rep(NA,NSims)
NEff=rep(NA,NSims)
DoseStore=rep(NA,Nmax)
YEStore = rep(NA,Nmax)
YTStore =rep(NA,Nmax)
B=2000
for(m1 in 1:NSims){
if(m1%%1000==0){
cat(paste(m1,"Simulations
",sep=" "))
}
Dosetried=c(1,rep(0,nDose-1))
##Start at lowest dose
Doses=rep(1,cohort)
OUT <- GETBIN(PROBS[1,])
YE=OUT[1]
YT=OUT[2]
if(cohort>1){
for(i in 2:cohort){
OUT <- GETBIN(t(PROBS[1,]))
YE=c(YE,OUT[1])
YT=c(YT,OUT[2])
}
}
##Ok now we have the starting values
for(i in 2:(Nmax/cohort)){
Z=UTEFFTOX(YE,YT, Doses, HypermeansE, HypermeansT, Hypervars, B )
MeanUT=rep(0,nDose)
Sigma=matrix(c(Hypervars[1]+Hypervars[3],Hypervars[3]*mean(Z[[3]]),
Hypervars[3]*mean(Z[[3]]), Hypervars[1]+Hypervars[3]), nrow=2, byrow=TRUE)
PMAT=Sigma
MeanUT=rep(0,nDose)
for(D in 1:nDose){
MU=c(mean(Z[[1]][,D]),mean(Z[[2]][,D]))
PEFF = 1-pnorm(0,MU[1],sqrt(Hypervars[1]+Hypervars[3]))
PTOX = 1-pnorm(0,MU[2],sqrt(Hypervars[1]+Hypervars[3]))
MeanUT[D]=GetDesire(PEFF,PTOX,Contour)
}
ACCEPTE=rep(1,nDose)
ACCEPTT=rep(1,nDose)
##What Doses are acceptable?
for(D in 1:nDose){
##Calculate the probability of eff over all the samples
PEFF = 1-pnorm(0,Z[[1]][,D],sqrt(Hypervars[1]+Hypervars[3]))
if(sum(Doses==D)>=cohort){
##IS THIS DOSE ACCEPTABLE IN TERMS OF EFFICACY?
ACCEPTE[D]=mean(PEFF>=CutE)>AcceptE
}
##Calculate the probability of tox over all the samples
PTOX = 1-pnorm(0,Z[[2]][,D],sqrt(Hypervars[1]+Hypervars[3]))
##IS THIS DOSE ACCEPTABLE IN TERM OF TOXICITY?
ACCEPTT[D]=mean(PTOX<=CutT)>AcceptT
}
## ACCEPTE[which(sum(Doses)<cohort)]=1
if(sum(ACCEPTE*ACCEPTT)==0 && i>2){
OptDose=0
break
}else{
##Multiply MeanUT by accept
for(j in 1:nDose){
if(ACCEPTE[j]*ACCEPTT[j]==0){
MeanUT[j]=-1000
}}
##Assign Next Dose Deterministically
OptDose = ReturnOpt(MeanUT,Dosetried) +1
Dosetried[OptDose]=1
##Now enroll the next three patients
Doses=c(Doses,rep(OptDose,cohort))
#What are their binary outcomes
for(m in 1:cohort){
OUT <- GETBIN(PROBS[OptDose,])
YE=c(YE,OUT[1])
YT=c(YT,OUT[2])
}
}
}
if(OptDose==0){
NSkip=NSkip+1
##This simulation doesn't count now.
X1=as.list(c(0,0,0))
X1[[1]]=OptDose
X1[[2]]=MeanUT
X1[[3]]=cbind(Doses,YE,YT)
SIMSTORE[[m1]]=X1
NTox[m1]=sum(YT)
NEff[m1]=sum(YE)
DoseOpt[m1]=OptDose
for(j in 1:nDose){
NumTrt[m1,j]=sum(Doses==j)
}
}else{
##Ok now weve done the whole trial, whats the optimal dose
Z=UTEFFTOX(YE,YT, Doses, HypermeansE, HypermeansT, Hypervars, B )
MeanUT=rep(0,nDose)
Sigma=matrix(c(Hypervars[1]+Hypervars[3],Hypervars[3]*mean(Z[[3]]),
Hypervars[3]*mean(Z[[3]]), Hypervars[1]+Hypervars[3]), nrow=2, byrow=TRUE)
PMAT=Sigma
for(D in 1:nDose){
MU=c(mean(Z[[1]][,D]),mean(Z[[2]][,D]))
PEFF = 1-pnorm(0,MU[1],sqrt(Hypervars[1]+Hypervars[3]))
PTOX = 1-pnorm(0,MU[2],sqrt(Hypervars[1]+Hypervars[3]))
MeanUT[D]=GetDesire(PEFF,PTOX,Contour)
}
ACCEPTE=rep(1,nDose)
ACCEPTT=rep(1,nDose)
##What Doses are acceptable?
for(D in 1:nDose){
##Calculate the probability of eff over all the samples
PEFF = 1-pnorm(0,Z[[1]][,D],sqrt(Hypervars[1]+Hypervars[3]))
if(sum(Doses==D)>=cohort){
##IS THIS DOSE ACCEPTABLE IN TERMS OF EFFICACY?
ACCEPTE[D]=mean(PEFF>=CutE)>AcceptE
}
##Calculate the probability of tox over all the samples
PTOX = 1-pnorm(0,Z[[2]][,D],sqrt(Hypervars[1]+Hypervars[3]))
##IS THIS DOSE ACCEPTABLE IN TERM OF TOXICITY?
ACCEPTT[D]=mean(PTOX<=CutT)>AcceptT
}
##Multiply MeanUT by accept
if(sum(ACCEPTE*ACCEPTT)==0){
OptDose=0
}else{
for(j in 1:nDose){
if(ACCEPTE[j]*ACCEPTT[j]*Dosetried[j]==0){
MeanUT[j]=-1000
}}
##Assign Next Dose Deterministically
OptDose = which(MeanUT==max(MeanUT))
}
##Fill this list with our values
X1=as.list(c(0,0,0))
X1[[1]]=min(OptDose)
X1[[2]]=MeanUT
X1[[3]]=cbind(Doses,YE,YT)
SIMSTORE[[m1]]=X1
NTox[m1]=sum(YT)
NEff[m1]=sum(YE)
for(j in 1:nDose){
NumTrt[m1,j]=sum(Doses==j)
}
DoseOpt[m1]=min(OptDose)
}
}
##Calculate True Accept
TRUECEPT = (PE>=CutE)*(PT<=CutT)
##These are the doses that are truly acceptable
##Calculate the true desireability
##Shift Desire to 0-100 range
desire=TRUEUT
for(j in 1:length(desire)){
desire[j]=GetDesire(PE[j],PT[j],Contour)
}
##desire = 50*desire+50
desire = 100*(desire - GetDesire(0,1,Contour))/(1-GetDesire(0,1,Contour))
TRUECEPT = (PE>=CutE)*(PT<=CutT)
##These are the doses that are truly acceptable
prob1=rep(0,nDose)
for(j in 1:nDose){
prob1[j]=mean(DoseOpt==j)
}
X=rbind(1:nDose,round(PT,2),round(PE,2),TRUECEPT,round(desire,2),prob1,round(colMeans(NumTrt),2))
rownames(X)=c("Dose #","True Toxicity Probability:","True Efficacy Probability:",
"Is the dose acceptable?", "True Desirability:",
"Dose Selection Probability:", "Average # Treated:")
colnames(X)=1:nDose
Z=as.list(c(0,0))
Z[[1]]=X
##Next we need the Single values
X1=c(mean(DoseOpt==0),mean(NEff),mean(NTox))
TRUECEPT = (PE>=CutE)*(PT<=CutT)
##Utilities of ACCEPTABLE doses
U1=desire*TRUECEPT
X1=c(corET,X1,round(sum(prob1*(U1-min(U1))/(max(U1)-min(U1))),2))
X1=as.matrix(X1)
rownames(X1)=c("CorET","P[Stop]","NEff","NTox","Delta")
Z[[2]]=X1
return(Z)
}
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