inst/example_COBA.R
In paulowhite/DelayedGSD: Planning and analysis of group sequential trial with pipeline data
rm(list=ls())
method <- 1:3 # methods used to compute the boundaries
#---
#--- to plan the trial ----
kMax <- 2 #max number of analyses (including final)
alpha <- 0.025 #type I error (one sided)
beta <- 0.2 #type II error
informationRates <- c(0.6,1) #planned information rates
rho_alpha <- 2 # rho parameter for alpha error spending function
rho_beta <- 2 # rho parameter for beta error spending function
## deltaPower <- 0.75 # just to try another value when Id > Imax
Id <- 0.75 #(expected) information rate at each decision analysis
binding <- FALSE
#
#---- to generate data -----------
#
block <- c(1,1,0,0)
allsd <- c(2.5,2.1,2.4) # sd, first from baseline measurement, then the two changes from baseline
mean0 <- c(10,0,0) # mean placebo group (again, first is absolute value, then change from baseline)
delta <- c(0,0.6,0.8) # treatment effect
ar <- (0.86*2)*2*2 # orginial accrual rate from data from Corine is 0.86 per week, hence we multiply by 2 for by 14 days. As too low, we further multiply by 2
cor011 <- -0.15 # ~ from data from Corine
corij1 <- 0.68 # ~ from data from Corine
cor0j1 <- -0.27 # ~ from data from Corine
Miss11 <- 5/104 # miss both V1 and V2
Miss12 <- 1/104 # miss V1 and but not V2
Miss21 <- 6/104 # do not miss V1 and but miss V2
Miss22 <- 92/104 # miss none
PropForInterim <- 0.5 # Decide to have interim analysiz when PropForInterim % of all subjects have had the chance to have one follow-up measuement recorded in the data to be available for analysis.
theDelta.t <- 1.50001 # time lag to process the data and make them ready to analyze after collecting them (unit is time between two follow-up visits)
TimeFactor <- 14 ## number of days between two visits
#
#--- actually for both planing the trial and generating data-----
#
#
deltaPower <- 1 # effect (NOT Z-scale/unit, but outcome scale/unit!) that the study is powered for: should we choose ourselves or compute from other numbers above ???
sdPower <- 2.5
n <- ceiling(2*2*((sdPower/deltaPower)^2)*(qnorm(1-beta)-qnorm(alpha))^2) #104 with Corine's data # should we choose ourselves or compute from the above numbers ???
# inflate SS as required for interim
#adjust for expected withdrawal
n <- n/(1-(Miss11+Miss21))
#library(devtools)
#install_github("PauloWhite/DelayedGSD")
#library(DelayedGSD)
sourceDir <- function(path, trace = TRUE, ...) {
for (nm in list.files(path, pattern = "[.][RrSsQq]$")) {
if(trace) cat(nm,":")
source(file.path(path, nm), ...)
if(trace) cat("\n")
}
}
sourceDir("R")
plannedB <- vector(mode = "list", length = 3)
for(iMeth in method){ ## iMeth <- 1
plannedB[[iMeth]] <- CalcBoundaries(kMax=kMax,
alpha=alpha,
beta=beta,
InfoR.i=informationRates,
InfoR.d=c(Id,1),
rho_alpha=rho_alpha,
rho_beta=rho_beta,
method=iMeth,
cNotBelowFixedc=FALSE,
bindingFutility=binding,
delta=deltaPower)
## summary(plannedB[[1]])
## coef(plannedB[[iMeth]], type = "information")
}
plot(plannedB[[1]])
png(file="planned_bounds_poster.png")
plot(plannedB[[3]])
dev.off()
inflationFactor <- unlist(lapply(plannedB,function(iP){iP$planned$InflationFactor}))
nGSD <- ceiling(n*inflationFactor)
myseedi <- 22525
# {{{ Missing probabilities
MyMissProb <- matrix(c(Miss11,Miss12,Miss21,Miss22),ncol=2,nrow=2,byrow=TRUE) # to additionnally remove 1 more because some FASFL=N
colnames(MyMissProb) <- c("V2 missing","V2 not missing")
rownames(MyMissProb) <- c("V1 missing","V1 not missing")
# }}}
# {{{ generate data
## ** simulate
res <- GenData(n=max(nGSD),
N.fw=2,
rand.block=block,
allsd=allsd,
mean0=mean0,
delta=delta,
ar=ar,
cor.01.1=cor011,
cor.ij.1=corij1,
cor.0j.1=cor0j1,
seed=myseedi,
MissProb=MyMissProb,
DigitsOutcome=2,
TimeFactor=TimeFactor,
DigitsTime=0
)
d <- res$d
## head(d,n=20)
# }}}
# {{{ reformat data like those of Corine
## Make data long format
## dd <- FormatAsCase(d)
## head(dd)
## summary(dd)
# }}}
nX1.interim <- vector()
nX2.interim <- vector()
nX3.interim <- vector()
currentGSD <- vector(mode = "list", length = 3)
out.interim <- vector(mode = "list", length = 3)
thets <- c()
for(iMeth in method){ ## iMeth <- 1
# {{{ make data available at interim
# Here we stop inclusion data collection for the interim analysis as soon as
# half of the participants have completed (or had the opportunity to complete) the follow-up
thets[iMeth] <- d$t3[ceiling(nGSD[iMeth]*PropForInterim)]
di <- SelectData(d,t=thets[iMeth])
## ddi <- FormatAsCase(di) # needed ????
## head(d[d$id==52,])
# }}}
nX1.interim[iMeth] <- sum(!is.na(di$X1))
nX2.interim[iMeth] = sum(!is.na(di$X2))
nX3.interim[iMeth] = sum(!is.na(di$X3))
## {{{ analyze data at at interim
## ** interim
lmmI <- analyzeData(di, ddf = "nlme", data.decision = sum(d$t1 <= thets[iMeth] + theDelta.t*TimeFactor), getinfo = TRUE, trace = TRUE)
currentGSD[[iMeth]] <- update(plannedB[[iMeth]], delta = lmmI, trace = FALSE)
iConfint.interim <- confint(currentGSD[[iMeth]])
iInfo.interim <- coef(currentGSD[[iMeth]], type = "information")
iBoundary.interim <- coef(currentGSD[[iMeth]], type = "boundary")
iDecision.interim <- coef(currentGSD[[iMeth]], type = "decision")
out.interim[[iMeth]] <- data.frame(statistic = iConfint.interim[1,"statistic"],
estimate_ML = iConfint.interim[1,"estimate"],
se_ML = iConfint.interim[1,"se"],
info = iInfo.interim[1,"Interim"],
infoPC = iInfo.interim[1,"Interim.pc"],
info.pred = iInfo.interim[1,"Decision"],
infoPC.pred = iInfo.interim[1,"Decision.pc"],
uk = iBoundary.interim[1,"Ebound"],
lk = iBoundary.interim[1,"Fbound"],
decision = iDecision.interim["decision","stage 1"],
reason = iDecision.interim["reason.interim","stage 1"])
}
## currentGSD[[1]]
png(filename = "plot_interim_poster.png")
plot(currentGSD[[3]])
dev.off()
PlotProgress(di)
out.decision <- vector(mode = "list", length = 3)
for(iMeth in method){ ## iMeth <- 1
## ** decision
dDecision <- d[which(d$t1 <= thets[iMeth] + theDelta.t*TimeFactor),]
lmmD <- analyzeData(dDecision, ddf = "nlme", getinfo = TRUE, trace = TRUE)
if(out.interim[[iMeth]]$decision == "stop"){
currentGSD[[iMeth]] <- update(currentGSD[[iMeth]], delta = lmmD, trace = FALSE)
## plot(currentGSD[[iMeth]])
iConfint.decision <- confint(currentGSD[[iMeth]], method = c("ML","MUE"))
iInfo.decision <- coef(currentGSD[[iMeth]], type = "information")
iBoundary.decision <- coef(currentGSD[[iMeth]], type = "boundary")
iDecision.decision <- coef(currentGSD[[iMeth]], type = "decision")
out.decision[[iMeth]] <- data.frame(statistic = iConfint.decision[1,"statistic"],
p.value_ML = iConfint.decision[iConfint.decision$method == "ML","p.value"],
lower_ML = iConfint.decision[iConfint.decision$method == "ML","lower"],
upper_ML = iConfint.decision[iConfint.decision$method == "ML","upper"],
estimate_ML = iConfint.decision[iConfint.decision$method == "ML","estimate"],
p.value_MUE = iConfint.decision[iConfint.decision$method == "MUE","p.value"],
lower_MUE = iConfint.decision[iConfint.decision$method == "MUE","lower"],
upper_MUE = iConfint.decision[iConfint.decision$method == "MUE","upper"],
estimate_MUE = iConfint.decision[iConfint.decision$method == "MUE","estimate"],
info = iInfo.decision[1,"Decision"],
infoPC = iInfo.decision[1,"Decision.pc"],
ck = iBoundary.decision[1,"Cbound"],
decision = unname(iDecision.decision["decision","stage 2"])
)
}else{
## update information
currentGSD[[iMeth]] <- update(currentGSD[[iMeth]], delta = lmmD, k = 1, type.k = "decision", trace = FALSE)
iInfo.decision <- coef(currentGSD[[iMeth]], type = "information")
iBoundary.decision <- coef(currentGSD[[iMeth]], type = "boundary")
out.decision[[iMeth]] <- data.frame(statistic = NA,
p.value_ML = NA,
lower_ML = NA,
upper_ML = NA,
estimate_ML = NA,
p.value_MUE = NA,
lower_MUE = NA,
upper_MUE = NA,
estimate_MUE = NA,
info = iInfo.decision[1,"Decision"],
infoPC = iInfo.decision[1,"Decision.pc"],
ck = iBoundary.decision[1,"Cbound"],
decision = NA)
}
}
# }}}
# {{{ Analyze data at decision
png("final_decision_poster.png")
plot(currentGSD[[3]])
dev.off()
## ** finale
out.final <- vector(mode = "list", length = 3)
for(iMeth in method){ ## iMeth <- 1
dFinal <- d[1:nGSD[iMeth],]
lmmF <- analyzeData(dFinal, ddf = "nlme", getinfo = TRUE, trace = TRUE)
if(out.interim[[iMeth]]$decision == "stop"){
out.final[[iMeth]] <- data.frame(statistic = NA,
p.value_ML = NA,
lower_ML = NA,
upper_ML = NA,
estimate_ML = NA,
p.value_MUE = NA,
lower_MUE = NA,
upper_MUE = NA,
estimate_MUE = NA,
info = NA,
infoPC = NA,
ck = NA,
decision = NA)
}else{
currentGSD[[iMeth]] <- update(currentGSD[[iMeth]], delta = lmmF, trace = FALSE)
## plot(test)
## summary(test)
iConfint.final <- confint(currentGSD[[iMeth]], method = c("ML","MUE"))
iInfo.final <- coef(currentGSD[[iMeth]], type = "information")
iBoundary.final <- coef(currentGSD[[iMeth]], type = "boundary")
iDecision.final <- coef(currentGSD[[iMeth]], type = "decision")
out.final[[iMeth]] <- data.frame(statistic = iConfint.final[1,"statistic"],
p.value_ML = iConfint.final[iConfint.final$method == "ML","p.value"],
lower_ML = iConfint.final[iConfint.final$method == "ML","lower"],
upper_ML = iConfint.final[iConfint.final$method == "ML","upper"],
estimate_ML = iConfint.final[iConfint.final$method == "ML","estimate"],
p.value_MUE = iConfint.final[iConfint.final$method == "MUE","p.value"],
lower_MUE = iConfint.final[iConfint.final$method == "MUE","lower"],
upper_MUE = iConfint.final[iConfint.final$method == "MUE","upper"],
estimate_MUE = iConfint.final[iConfint.final$method == "MUE","estimate"],
info = iInfo.final[2,"Interim"], #COBA: shouldn't this be taken from row 2?
infoPC = iInfo.final[2,"Interim.pc"], #COBA: shouldn't this be taken from row 2?
ck = iBoundary.final[2,"Cbound"], #COBA: shouldn't this be taken from row 2?
decision = unname(coef(currentGSD[[iMeth]], type = "decision")["decision","stage 2"])
)
}
}
# }}}
outMerge <- do.call(rbind,lapply(method, function(iMeth){
iNames <- unique(c(names(out.interim[[iMeth]]),names(out.decision[[iMeth]]),names(out.final[[iMeth]])))
iMerge <- data.frame(matrix(NA, ncol = length(iNames)+3, nrow = 3, dimnames = list(NULL, c("method", "stage", "type", iNames))))
iMerge[1,c("method","stage","type",names(out.interim[[iMeth]]))] <- data.frame(method = iMeth, stage = 1, type = "interim", out.interim[[iMeth]])
iMerge[2,c("method","stage","type",names(out.decision[[iMeth]]))] <- data.frame(method = iMeth, stage = 1, type = "decision", out.decision[[iMeth]])
iMerge[3,c("method","stage","type",names(out.final[[iMeth]]))] <- data.frame(method = iMeth, stage = 2, type = "final", out.final[[iMeth]])
return(iMerge)
}))
## outMerge[outMerge$method==3,]
out <- cbind(
## results
outMerge,
## simulation details
time.interim = rep(thets,each=3),
total.n = rep(nGSD,each=3),
nX1.interim = rep(nX1.interim,each=3),
nX2.interim = rep(nX2.interim,each=3),
nX3.interim = rep(nX3.interim,each=3),
seed=myseedi
)
paulowhite/DelayedGSD documentation built on Nov. 1, 2023, 5:36 p.m.
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rm(list=ls())
method <- 1:3 # methods used to compute the boundaries
#---
#--- to plan the trial ----
kMax <- 2 #max number of analyses (including final)
alpha <- 0.025 #type I error (one sided)
beta <- 0.2 #type II error
informationRates <- c(0.6,1) #planned information rates
rho_alpha <- 2 # rho parameter for alpha error spending function
rho_beta <- 2 # rho parameter for beta error spending function
## deltaPower <- 0.75 # just to try another value when Id > Imax
Id <- 0.75 #(expected) information rate at each decision analysis
binding <- FALSE
#
#---- to generate data -----------
#
block <- c(1,1,0,0)
allsd <- c(2.5,2.1,2.4) # sd, first from baseline measurement, then the two changes from baseline
mean0 <- c(10,0,0) # mean placebo group (again, first is absolute value, then change from baseline)
delta <- c(0,0.6,0.8) # treatment effect
ar <- (0.86*2)*2*2 # orginial accrual rate from data from Corine is 0.86 per week, hence we multiply by 2 for by 14 days. As too low, we further multiply by 2
cor011 <- -0.15 # ~ from data from Corine
corij1 <- 0.68 # ~ from data from Corine
cor0j1 <- -0.27 # ~ from data from Corine
Miss11 <- 5/104 # miss both V1 and V2
Miss12 <- 1/104 # miss V1 and but not V2
Miss21 <- 6/104 # do not miss V1 and but miss V2
Miss22 <- 92/104 # miss none
PropForInterim <- 0.5 # Decide to have interim analysiz when PropForInterim % of all subjects have had the chance to have one follow-up measuement recorded in the data to be available for analysis.
theDelta.t <- 1.50001 # time lag to process the data and make them ready to analyze after collecting them (unit is time between two follow-up visits)
TimeFactor <- 14 ## number of days between two visits
#
#--- actually for both planing the trial and generating data-----
#
#
deltaPower <- 1 # effect (NOT Z-scale/unit, but outcome scale/unit!) that the study is powered for: should we choose ourselves or compute from other numbers above ???
sdPower <- 2.5
n <- ceiling(2*2*((sdPower/deltaPower)^2)*(qnorm(1-beta)-qnorm(alpha))^2) #104 with Corine's data # should we choose ourselves or compute from the above numbers ???
# inflate SS as required for interim
#adjust for expected withdrawal
n <- n/(1-(Miss11+Miss21))
#library(devtools)
#install_github("PauloWhite/DelayedGSD")
#library(DelayedGSD)
sourceDir <- function(path, trace = TRUE, ...) {
for (nm in list.files(path, pattern = "[.][RrSsQq]$")) {
if(trace) cat(nm,":")
source(file.path(path, nm), ...)
if(trace) cat("\n")
}
}
sourceDir("R")
plannedB <- vector(mode = "list", length = 3)
for(iMeth in method){ ## iMeth <- 1
plannedB[[iMeth]] <- CalcBoundaries(kMax=kMax,
alpha=alpha,
beta=beta,
InfoR.i=informationRates,
InfoR.d=c(Id,1),
rho_alpha=rho_alpha,
rho_beta=rho_beta,
method=iMeth,
cNotBelowFixedc=FALSE,
bindingFutility=binding,
delta=deltaPower)
## summary(plannedB[[1]])
## coef(plannedB[[iMeth]], type = "information")
}
plot(plannedB[[1]])
png(file="planned_bounds_poster.png")
plot(plannedB[[3]])
dev.off()
inflationFactor <- unlist(lapply(plannedB,function(iP){iP$planned$InflationFactor}))
nGSD <- ceiling(n*inflationFactor)
myseedi <- 22525
# {{{ Missing probabilities
MyMissProb <- matrix(c(Miss11,Miss12,Miss21,Miss22),ncol=2,nrow=2,byrow=TRUE) # to additionnally remove 1 more because some FASFL=N
colnames(MyMissProb) <- c("V2 missing","V2 not missing")
rownames(MyMissProb) <- c("V1 missing","V1 not missing")
# }}}
# {{{ generate data
## ** simulate
res <- GenData(n=max(nGSD),
N.fw=2,
rand.block=block,
allsd=allsd,
mean0=mean0,
delta=delta,
ar=ar,
cor.01.1=cor011,
cor.ij.1=corij1,
cor.0j.1=cor0j1,
seed=myseedi,
MissProb=MyMissProb,
DigitsOutcome=2,
TimeFactor=TimeFactor,
DigitsTime=0
)
d <- res$d
## head(d,n=20)
# }}}
# {{{ reformat data like those of Corine
## Make data long format
## dd <- FormatAsCase(d)
## head(dd)
## summary(dd)
# }}}
nX1.interim <- vector()
nX2.interim <- vector()
nX3.interim <- vector()
currentGSD <- vector(mode = "list", length = 3)
out.interim <- vector(mode = "list", length = 3)
thets <- c()
for(iMeth in method){ ## iMeth <- 1
# {{{ make data available at interim
# Here we stop inclusion data collection for the interim analysis as soon as
# half of the participants have completed (or had the opportunity to complete) the follow-up
thets[iMeth] <- d$t3[ceiling(nGSD[iMeth]*PropForInterim)]
di <- SelectData(d,t=thets[iMeth])
## ddi <- FormatAsCase(di) # needed ????
## head(d[d$id==52,])
# }}}
nX1.interim[iMeth] <- sum(!is.na(di$X1))
nX2.interim[iMeth] = sum(!is.na(di$X2))
nX3.interim[iMeth] = sum(!is.na(di$X3))
## {{{ analyze data at at interim
## ** interim
lmmI <- analyzeData(di, ddf = "nlme", data.decision = sum(d$t1 <= thets[iMeth] + theDelta.t*TimeFactor), getinfo = TRUE, trace = TRUE)
currentGSD[[iMeth]] <- update(plannedB[[iMeth]], delta = lmmI, trace = FALSE)
iConfint.interim <- confint(currentGSD[[iMeth]])
iInfo.interim <- coef(currentGSD[[iMeth]], type = "information")
iBoundary.interim <- coef(currentGSD[[iMeth]], type = "boundary")
iDecision.interim <- coef(currentGSD[[iMeth]], type = "decision")
out.interim[[iMeth]] <- data.frame(statistic = iConfint.interim[1,"statistic"],
estimate_ML = iConfint.interim[1,"estimate"],
se_ML = iConfint.interim[1,"se"],
info = iInfo.interim[1,"Interim"],
infoPC = iInfo.interim[1,"Interim.pc"],
info.pred = iInfo.interim[1,"Decision"],
infoPC.pred = iInfo.interim[1,"Decision.pc"],
uk = iBoundary.interim[1,"Ebound"],
lk = iBoundary.interim[1,"Fbound"],
decision = iDecision.interim["decision","stage 1"],
reason = iDecision.interim["reason.interim","stage 1"])
}
## currentGSD[[1]]
png(filename = "plot_interim_poster.png")
plot(currentGSD[[3]])
dev.off()
PlotProgress(di)
out.decision <- vector(mode = "list", length = 3)
for(iMeth in method){ ## iMeth <- 1
## ** decision
dDecision <- d[which(d$t1 <= thets[iMeth] + theDelta.t*TimeFactor),]
lmmD <- analyzeData(dDecision, ddf = "nlme", getinfo = TRUE, trace = TRUE)
if(out.interim[[iMeth]]$decision == "stop"){
currentGSD[[iMeth]] <- update(currentGSD[[iMeth]], delta = lmmD, trace = FALSE)
## plot(currentGSD[[iMeth]])
iConfint.decision <- confint(currentGSD[[iMeth]], method = c("ML","MUE"))
iInfo.decision <- coef(currentGSD[[iMeth]], type = "information")
iBoundary.decision <- coef(currentGSD[[iMeth]], type = "boundary")
iDecision.decision <- coef(currentGSD[[iMeth]], type = "decision")
out.decision[[iMeth]] <- data.frame(statistic = iConfint.decision[1,"statistic"],
p.value_ML = iConfint.decision[iConfint.decision$method == "ML","p.value"],
lower_ML = iConfint.decision[iConfint.decision$method == "ML","lower"],
upper_ML = iConfint.decision[iConfint.decision$method == "ML","upper"],
estimate_ML = iConfint.decision[iConfint.decision$method == "ML","estimate"],
p.value_MUE = iConfint.decision[iConfint.decision$method == "MUE","p.value"],
lower_MUE = iConfint.decision[iConfint.decision$method == "MUE","lower"],
upper_MUE = iConfint.decision[iConfint.decision$method == "MUE","upper"],
estimate_MUE = iConfint.decision[iConfint.decision$method == "MUE","estimate"],
info = iInfo.decision[1,"Decision"],
infoPC = iInfo.decision[1,"Decision.pc"],
ck = iBoundary.decision[1,"Cbound"],
decision = unname(iDecision.decision["decision","stage 2"])
)
}else{
## update information
currentGSD[[iMeth]] <- update(currentGSD[[iMeth]], delta = lmmD, k = 1, type.k = "decision", trace = FALSE)
iInfo.decision <- coef(currentGSD[[iMeth]], type = "information")
iBoundary.decision <- coef(currentGSD[[iMeth]], type = "boundary")
out.decision[[iMeth]] <- data.frame(statistic = NA,
p.value_ML = NA,
lower_ML = NA,
upper_ML = NA,
estimate_ML = NA,
p.value_MUE = NA,
lower_MUE = NA,
upper_MUE = NA,
estimate_MUE = NA,
info = iInfo.decision[1,"Decision"],
infoPC = iInfo.decision[1,"Decision.pc"],
ck = iBoundary.decision[1,"Cbound"],
decision = NA)
}
}
# }}}
# {{{ Analyze data at decision
png("final_decision_poster.png")
plot(currentGSD[[3]])
dev.off()
## ** finale
out.final <- vector(mode = "list", length = 3)
for(iMeth in method){ ## iMeth <- 1
dFinal <- d[1:nGSD[iMeth],]
lmmF <- analyzeData(dFinal, ddf = "nlme", getinfo = TRUE, trace = TRUE)
if(out.interim[[iMeth]]$decision == "stop"){
out.final[[iMeth]] <- data.frame(statistic = NA,
p.value_ML = NA,
lower_ML = NA,
upper_ML = NA,
estimate_ML = NA,
p.value_MUE = NA,
lower_MUE = NA,
upper_MUE = NA,
estimate_MUE = NA,
info = NA,
infoPC = NA,
ck = NA,
decision = NA)
}else{
currentGSD[[iMeth]] <- update(currentGSD[[iMeth]], delta = lmmF, trace = FALSE)
## plot(test)
## summary(test)
iConfint.final <- confint(currentGSD[[iMeth]], method = c("ML","MUE"))
iInfo.final <- coef(currentGSD[[iMeth]], type = "information")
iBoundary.final <- coef(currentGSD[[iMeth]], type = "boundary")
iDecision.final <- coef(currentGSD[[iMeth]], type = "decision")
out.final[[iMeth]] <- data.frame(statistic = iConfint.final[1,"statistic"],
p.value_ML = iConfint.final[iConfint.final$method == "ML","p.value"],
lower_ML = iConfint.final[iConfint.final$method == "ML","lower"],
upper_ML = iConfint.final[iConfint.final$method == "ML","upper"],
estimate_ML = iConfint.final[iConfint.final$method == "ML","estimate"],
p.value_MUE = iConfint.final[iConfint.final$method == "MUE","p.value"],
lower_MUE = iConfint.final[iConfint.final$method == "MUE","lower"],
upper_MUE = iConfint.final[iConfint.final$method == "MUE","upper"],
estimate_MUE = iConfint.final[iConfint.final$method == "MUE","estimate"],
info = iInfo.final[2,"Interim"], #COBA: shouldn't this be taken from row 2?
infoPC = iInfo.final[2,"Interim.pc"], #COBA: shouldn't this be taken from row 2?
ck = iBoundary.final[2,"Cbound"], #COBA: shouldn't this be taken from row 2?
decision = unname(coef(currentGSD[[iMeth]], type = "decision")["decision","stage 2"])
)
}
}
# }}}
outMerge <- do.call(rbind,lapply(method, function(iMeth){
iNames <- unique(c(names(out.interim[[iMeth]]),names(out.decision[[iMeth]]),names(out.final[[iMeth]])))
iMerge <- data.frame(matrix(NA, ncol = length(iNames)+3, nrow = 3, dimnames = list(NULL, c("method", "stage", "type", iNames))))
iMerge[1,c("method","stage","type",names(out.interim[[iMeth]]))] <- data.frame(method = iMeth, stage = 1, type = "interim", out.interim[[iMeth]])
iMerge[2,c("method","stage","type",names(out.decision[[iMeth]]))] <- data.frame(method = iMeth, stage = 1, type = "decision", out.decision[[iMeth]])
iMerge[3,c("method","stage","type",names(out.final[[iMeth]]))] <- data.frame(method = iMeth, stage = 2, type = "final", out.final[[iMeth]])
return(iMerge)
}))
## outMerge[outMerge$method==3,]
out <- cbind(
## results
outMerge,
## simulation details
time.interim = rep(thets,each=3),
total.n = rep(nGSD,each=3),
nX1.interim = rep(nX1.interim,each=3),
nX2.interim = rep(nX2.interim,each=3),
nX3.interim = rep(nX3.interim,each=3),
seed=myseedi
)
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