#Author: Joost van Rosmalen
#Date: September 22, 2016
#Description: Syntax for simulation study with different methods for
#including historical trials in the context of survival analysis.

rm(list=ls(all=TRUE))  #Clear workspace

#Load packages
library(foreign)
library(survival)
library(stats4)
library(rjags)
library(mcmc)
library(MCMCpack)
library(cubature)
library(R2Cuba)
library(R2jags)
library(Hmisc)
library(gtools)
library(R.utils)
library(plyr)
library(dplyr)
library(parallel)

#Settings of simulation study. These also need to be specified in the file SimulationOneDataSet.r
n_sims<- 500 #Number of simulated datasets per setting
weib_shape<- 0.6 #Shape parameter
cens_start<- 36 
cens_end <- 84 #All patients are censored between 36 and 84 months, using a uniform distribution
n_patients<- 150 #Number of patients in simulated data set
n_hist_trials <- 3 #Number of historical trials
niter<- 150000 #Number of MCMC iterations per data set
baseline_hazard <- -2.5 #Log baseline hazard
time_trend<- log(0.95) #Time trend per trial, on log-hazard scale

#Set working directories
directory_code<-"/home/jvanrosmalen/Joost/AnalysesRevisie/"
setwd(directory_code)

#Save output to text file.
sink(paste("results.txt"))

#Initialize counter of computation time
tt<-proc.time()

#Create the arrays with MCMC output
result_overall<-array(0,c(niter/5,ncol=10,n_sims))
result_mean<-matrix(0,6*ncol(result_overall),n_sims)
result_RMSE<-matrix(0,6*ncol(result_overall),n_sims)
result_SE<-matrix(0,6*ncol(result_overall),n_sims)
result_reject_null<-matrix(0,6*ncol(result_overall),n_sims)

#Now start the main analyses
for(setting in 1:2){
  if(setting==1){    
    logHR_intervention <- log(0.7) #Treatment effect
  }
  if(setting==2){    
    logHR_intervention <- 0 #No treatment effect, to assess type I error
  }
for (sc in c(1:6)){
  #Sc denotes the scenario of the simulation study
  #Values:
  #1: no time trend
  #2: time trend in data, without time included in analysis model
  #3: time trend in data, with time included in analysis model
  #4: no heterogeneity
  #5: high between-trial heterogeneity
  #6: very high between-trial heterogeneity

#Create the array with MCMC output
result_overall<-array(0,c(niter/5,ncol=10,n_sims))

source("SimulationOneDataSet.r",local=TRUE)

cl <- makeCluster(mc <- getOption("cl.cores", 75))
simVector<-1:n_sims
temp<-clusterApplyLB(cl, simVector, SimulationOneDataSet,sc=sc,logHR_intervention=logHR_intervention)
stopCluster(cl)
for(ii in 1:length(simVector)){
  result_overall[,,simVector[ii]]<-temp[[ii]][,]
}
rm(temp)

colnames(result_overall)<-c("Current data","Pooled data","Pocock's method","Power prior with $alpha=0.5$","MPP","MAP approach","Method of Murray et al.","Test-then-pool method","Robust MAP approach version 1","Robust MAP approach version 2")

for (i in 1:ncol(result_overall)){
  for(j in 1:n_sims){
      result_mean[i+ncol(result_overall)*(sc-1),j]<-mean(result_overall[,i,j])- logHR_intervention
      result_RMSE[i+ncol(result_overall)*(sc-1),j]<-sqrt(mean((result_overall[,i,j]- logHR_intervention)^2))
      result_SE[i+ncol(result_overall)*(sc-1),j]<-sqrt(var(result_overall[,i,j]))
      exceedance_prob<- mean(result_overall[,i,j]>0) #This is the one-sided Bayesian p-value of the intervention effect
      if(exceedance_prob>=0.025 & exceedance_prob<=0.975){
        result_reject_null[i+ncol(result_overall)*(sc-1),j]<- 0
      }
      else{
        result_reject_null[i+ncol(result_overall)*(sc-1),j]<- 1
      }
  }
}
rownames(result_mean)<-rep(colnames(result_overall),6)
rownames(result_RMSE)<-rep(colnames(result_overall),6)
rownames(result_SE)<-rep(colnames(result_overall),6)
rownames(result_reject_null)<-rep(colnames(result_overall),6)
print(result_mean)
print(result_RMSE)
print(result_SE)
print(result_reject_null)
result_mean_tot<-round(matrix(rowSums(result_mean)/ncol(result_mean),nrow=10,ncol=6),3)
rownames(result_mean_tot)<-rownames(result_mean)[1:10]
colnames(result_mean_tot)<-c("No time trend","Time trend, no adjustment","Time trend, with adjustment","No heterogeneity","High heterogeneity","Very high heterogeneity")
result_RMSE_tot<-round(matrix(rowSums(result_RMSE)/ncol(result_RMSE),nrow=10,ncol=6),3)
rownames(result_RMSE_tot)<-rownames(result_RMSE)[1:10]
colnames(result_RMSE_tot)<-c("No time trend","Time trend, no adjustment","Time trend, with adjustment","No heterogeneity","High heterogeneity","Very high heterogeneity")
result_SE_tot<-round(matrix(rowSums(result_SE)/ncol(result_SE),nrow=10,ncol=6),3)
rownames(result_SE_tot)<-rownames(result_SE)[1:10]
colnames(result_SE_tot)<-c("No time trend","Time trend, no adjustment","Time trend, with adjustment","No heterogeneity","High heterogeneity","Very high heterogeneity")
result_reject_null_tot<-round(matrix(rowSums(result_reject_null)/ncol(result_reject_null),nrow=10,ncol=6),6)
rownames(result_reject_null_tot)<-rownames(result_reject_null)[1:10]
colnames(result_reject_null_tot)<-c("No time trend","Time trend, no adjustment","Time trend, with adjustment","No heterogeneity","High heterogeneity","Very high heterogeneity")
print(result_mean_tot)
print(result_RMSE_tot)
print(result_SE_tot)
print(result_reject_null_tot)

cat(captureOutput(latex.default(result_mean_tot,file="",label="result_sim_mean",
                               caption="Average bias of estimated intervention effect in simulation study")),sep="\n")
cat(captureOutput(latex.default(result_RMSE_tot,file="",label="result_sim_RMSE",
                                caption="Average RMSD of estimated intervention effect in simulation study")),sep="\n")
cat(captureOutput(latex.default(result_SE_tot,file="",label="result_sim_SE",
                                caption="Average posterior standard deviation of estimated intervention effect in simulation study")),sep="\n")
cat(captureOutput(latex.default(result_reject_null_tot,file="",label="result_sim_reject_null",
                                caption="Power of estimated intervention effect in simulation study")),sep="\n")

result_mean_CIs<-data.frame(No_time_trend=rep("c",10),Time_trend_no_adjustment=rep("g",10),Time_trend_with_adjustment=rep("g",10),
                            No_heterogeneity=rep("g",10),High_heterogeneity=rep("g",10),Very_high_heterogeneity=rep("g",10), stringsAsFactors=FALSE)
rownames(result_mean_CIs)<-rownames(result_mean)[1:10]
for(ii in 1:10){
  for(jj in 1:6){
    result_mean_CIs[ii,jj]<-paste(toString(formatC(mean(result_mean[10*(jj-1)+ii,]),format="f",digits=3))," (",
                                  toString(formatC(mean(result_mean[10*(jj-1)+ii,])                                              
                                                   -qt(0.975,df=length(result_mean[10*(jj-1)+ii,])-1)*sd(result_mean[10*(jj-1)+ii,])/sqrt(length(result_mean[10*(jj-1)+ii,])),format="f",digits=3))                             
                                  ," - ",toString(formatC(mean(result_mean[10*(jj-1)+ii,])                                               
                                                          +qt(0.975,df=length(result_mean[10*(jj-1)+ii,])-1)*sd(result_mean[10*(jj-1)+ii,])/sqrt(length(result_mean[10*(jj-1)+ii,])),format="f",digits=3)) ,")",sep="")
  }  
}
result_mean_CIs
cat(captureOutput(latex.default(result_mean_CIs,file="",label="result_sim_mean",
                                caption="Average bias of estimated intervention effect in simulation study")),sep="\n")

result_RMSE_CIs<-data.frame(No_time_trend=rep("c",10),Time_trend_no_adjustment=rep("g",10),Time_trend_with_adjustment=rep("g",10),
                            No_heterogeneity=rep("g",10),High_heterogeneity=rep("g",10),Very_high_heterogeneity=rep("g",10), stringsAsFactors=FALSE)
rownames(result_RMSE_CIs)<-rownames(result_RMSE)[1:10]
for(ii in 1:10){
  for(jj in 1:6){
    result_RMSE_CIs[ii,jj]<-paste(toString(formatC(mean(result_RMSE[10*(jj-1)+ii,]),format="f",digits=3))," (",
                                  toString(formatC(mean(result_RMSE[10*(jj-1)+ii,])                                              
                                                   -qt(0.975,df=length(result_RMSE[10*(jj-1)+ii,])-1)*sd(result_RMSE[10*(jj-1)+ii,])/sqrt(length(result_RMSE[10*(jj-1)+ii,])),format="f",digits=3))                             
                                  ," - ",toString(formatC(mean(result_RMSE[10*(jj-1)+ii,])                                               
                                                          +qt(0.975,df=length(result_RMSE[10*(jj-1)+ii,])-1)*sd(result_RMSE[10*(jj-1)+ii,])/sqrt(length(result_RMSE[10*(jj-1)+ii,])),format="f",digits=3))                             
                                  
                                  ,")",sep="")
  }  
}
result_RMSE_CIs
cat(captureOutput(latex.default(result_RMSE_CIs,file="",label="result_sim_RMSE",
                                caption="Average RMSD of estimated intervention effect in simulation study")),sep="\n")

result_SE_CIs<-data.frame(No_time_trend=rep("c",10),Time_trend_no_adjustment=rep("g",10),Time_trend_with_adjustment=rep("g",10),
                          No_heterogeneity=rep("g",10),High_heterogeneity=rep("g",10),Very_high_heterogeneity=rep("g",10), stringsAsFactors=FALSE)
rownames(result_SE_CIs)<-rownames(result_SE)[1:10]
for(ii in 1:10){
  for(jj in 1:6){
    result_SE_CIs[ii,jj]<-paste(toString(formatC(mean(result_SE[10*(jj-1)+ii,]),format="f",digits=3))," (",
                                  toString(formatC(mean(result_SE[10*(jj-1)+ii,])                                              
                                                   -qt(0.975,df=length(result_SE[10*(jj-1)+ii,])-1)*sd(result_SE[10*(jj-1)+ii,])/sqrt(length(result_SE[10*(jj-1)+ii,])),format="f",digits=3))                             
                                  ," - ",toString(formatC(mean(result_SE[10*(jj-1)+ii,])                                               
                                                          +qt(0.975,df=length(result_SE[10*(jj-1)+ii,])-1)*sd(result_SE[10*(jj-1)+ii,])/sqrt(length(result_SE[10*(jj-1)+ii,])),format="f",digits=3))                             
                                  
                                  ,")",sep="")
  }  
}
result_SE_CIs
cat(captureOutput(latex.default(result_SE_CIs,file="",label="result_sim_SE",
                                caption="Average posterior standard deviation of estimated intervention effect in simulation study")),sep="\n")

result_power_CIs<-data.frame(No_time_trend=rep("c",10),Time_trend_no_adjustment=rep("g",10),Time_trend_with_adjustment=rep("g",10),
                          No_heterogeneity=rep("g",10),High_heterogeneity=rep("g",10),Very_high_heterogeneity=rep("g",10), stringsAsFactors=FALSE)
rownames(result_power_CIs)<-rownames(result_reject_null)[1:10]
for(ii in 1:10){
  for(jj in 1:6){
    result_power_CIs[ii,jj]<-paste(toString(formatC(mean(result_reject_null[10*(jj-1)+ii,]),format="f",digits=3)),sep="")
  }  
}
result_power_CIs
cat(captureOutput(latex.default(result_power_CIs,file="",label="result_sim_power",
                                caption="Power of estimated intervention effect in simulation study")),sep="\n")

result_sample_size_reduction<-data.frame(No_time_trend=rep("c",10),Time_trend_no_adjustment=rep("g",10),Time_trend_with_adjustment=rep("g",10),
                             No_heterogeneity=rep("g",10),High_heterogeneity=rep("g",10),Very_high_heterogeneity=rep("g",10), stringsAsFactors=FALSE)
rownames(result_sample_size_reduction)<-rownames(result_reject_null)[1:10]
for(ii in 1:10){
  for(jj in 1:6){
    result_sample_size_reduction[ii,jj]<-paste(toString(formatC(
      100-100*(qnorm(0.025)+qnorm(1-mean(result_reject_null[10*(jj-1)+1,])))^2/
      (qnorm(0.025)+qnorm(1-mean(result_reject_null[10*(jj-1)+ii,])))^2
      ,format="f",digits=1)),sep="")
  }  
}
result_sample_size_reduction
cat(captureOutput(latex.default(result_sample_size_reduction,file="",label="result_sim_sample_size_reduction",
                                caption="Estimated sample size reduction due to the use of historical data, based on power in simulation study")),sep="\n")

if(setting==1){
  save.image(file=paste(directory_code,"output_simstudy_treatment_effect", niter,".Rdata",sep=""))
}
if(setting==2){
  save.image(file=paste(directory_code,"output_simstudy_no_treatment_effect", niter,".Rdata",sep=""))
}

}
}
print(proc.time()-tt)

sink()