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JOINT_TITE_POCRM.R
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JOINT_TITE_POCRM.R
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#JOINT TITE-POCRM function
#part 2 is 2 parameter joint
#part 1 has 1 parameter independent
library(rjags)
library(cubature)
library(mvtnorm)
source("data_generation_TTE_v2.R")
##pre-amble functions
#logit & expit
logit<-function(p){
return(log(p/(1-p)))
}
expit<-function (x) {
return(exp(x)/(1 + exp(x)))
}
##this function breaks ties by choosing lowest as default (for comparability in sims), specify anything else in break.ties arg for random
which.is.max.func<-function(x,break.ties="min"){
if(break.ties=="min"){
if(is.vector(x)){
y<-seq_along(x)[x==max(x)]
if (length(y) > 1L)
return(y[1])
else return(y)
}else if(is.matrix(x)){
y<-which(x==max(x),arr.ind = T)
if(nrow(y)>1L){
return(y[1,])
}else{
return(y)
}
}
}else{
if(is.vector(x)){
y<-seq_along(x)[x==max(x)]
if (length(y) > 1L)
return(sample(y, 1L))
else return(y)
}else if(is.matrix(x)){
y<-which(x==max(x),arr.ind = T)
if(nrow(y)>1L){
return(y[sample(c(1:nrow(y)),1L),])
}else{
return(y)
}
}
}
}
#posterior as function of parameters, for integrations (independent, 1 par)
post.fun.ind.1par<-function(parameters,y,patdoses,wm_skeleton,weight,prior_hyp){
##parameter
alpha<-parameters
#prior hyper-parameters
mu_alpha<-prior_hyp[1]
tau_alpha<-prior_hyp[2]
G<-c()
pi<-c()
ndoses<-length(wm_skeleton)
for(j in 1:ndoses){
pi[j] <- (wm_skeleton[j])^exp(alpha)
}
for(i in 1:length(patdoses)){
G[i]<-weight[i]*pi[patdoses[i]]
}
prod((G^y)*((1-G)^(1-y)))*
dnorm(alpha, mean=mu_alpha,sd=sqrt(1/tau_alpha))
}
#vectorises for integration function
V.post.fun.ind.1par<-function(parameters,y,patdoses,wm_skeleton,weight,prior_hyp){
sapply(parameters,post.fun.ind.1par,y,patdoses,wm_skeleton,weight,prior_hyp)
}
#model for rjags
model.crm.joint.string <-"
model {
for(j in 1:ndoses){
logit(piT[j]) = alphaT + betaT*dosesT[j]
logit(piE[j]) = alphaE + betaE*dosesE[j]
}
for(i in 1:length(yOUT)){
G_piE[i]=weightE[i]*piE[patdoses[i]]
G_piT[i]=weightT[i]*piT[patdoses[i]]
pi_nE_nT[i] = (1-G_piE[i])*(1-G_piT[i]) + G_piE[i]*(1-G_piT[i])*G_piT[i]*(1-G_piE[i])*(exp(phi)-1)/(exp(phi)+1)
pi_nE_T[i] = (1-G_piE[i])*(G_piT[i]) - G_piE[i]*(1-G_piT[i])*G_piT[i]*(1-G_piE[i])*(exp(phi)-1)/(exp(phi)+1)
pi_E_nT[i] = (G_piE[i])*(1-G_piT[i]) - G_piE[i]*(1-G_piT[i])*G_piT[i]*(1-G_piE[i])*(exp(phi)-1)/(exp(phi)+1)
pi_E_T[i] = (G_piE[i])*(G_piT[i]) + G_piE[i]*(1-G_piT[i])*G_piT[i]*(1-G_piE[i])*(exp(phi)-1)/(exp(phi)+1)
yOUT[i] ~ dcat(c(pi_nE_nT[i],pi_nE_T[i],pi_E_nT[i],pi_E_T[i]))
}
log_betaT ~ dnorm(mu_betaT, tau_betaT)
betaT = exp(log_betaT)
alphaT ~ dnorm(mu_alphaT, tau_alphaT)
log_betaE ~ dnorm(mu_betaE, tau_betaE)
betaE = exp(log_betaE)
alphaE ~ dnorm(mu_alphaE, tau_alphaE)
phi~ dnorm(mu_phi,tau_phi)
}
"
##function for gibbs sampler
gibbs_sampler.crm.combo<-function(data_list,iter,model_string){
model1.spec<-textConnection(model_string)
jags <- jags.model(model1.spec,data =data_list,n.chains=1,n.adapt=1000,quiet=T)
update(jags, 1000,progress.bar="none")
tt<-jags.samples(jags,c('alphaT','betaT','alphaE','betaE','phi'),iter,progress.bar="none")
return(tt)
}
#defines category
category_function<-function(E_vec,T_vec){
length_out<-length(E_vec)
length_out2<-length(T_vec)
if(length_out!=length_out2){
stop("incompatible lengths")
}
out_vec<-c()
for(i in 1:length_out){
if(E_vec[i]+T_vec[i]==2){
out_vec[i]<-4
}else if(E_vec[i]+T_vec[i]==0){
out_vec[i]<-1
}else if((E_vec[i]==1)&(T_vec[i]==0)){
out_vec[i]<-3
}else if((E_vec[i]==0)&(T_vec[i]==1)){
out_vec[i]<-2
}
}
return(out_vec)
}
current_patient_data_frame<-function(current_time,patient.dataframe,follow_up){
#input the whole data and convert to format for TITECRM
#each row is a patient, with entries c("entry.time","dose.level","DLT","DLT.time","Eff","Eff.time")
num_patients<-max(patient.dataframe$patient_ID)
patient.dataframe<-patient.dataframe[patient.dataframe$time_of<=current_time,]
patient.dataframe<-patient.dataframe[patient.dataframe$cycle_num<=follow_up,]
patient_dataframe<-matrix(NA,nrow=num_patients,ncol=6)
for (i in 1:num_patients){
patient_data_ind<-patient.dataframe[patient.dataframe$patient_ID==i,]
patient_DLT<-max(patient_data_ind$DLT)
patient_Eff<-max(patient_data_ind$Eff)
patient_entry.time<-patient_data_ind$entry_time[1]
patient_dose.level<-patient_data_ind$dose_level[1]
if(patient_DLT==1){
patient_DLT.time<-max(patient_data_ind$DLT.time,na.rm=T)
}else{
patient_DLT.time<-NA
}
if(patient_Eff==1){
patient_Eff.time<-max(patient_data_ind$Eff.time,na.rm=T)
}else{
patient_Eff.time<-NA
}
patient_dataframe[i,]<-c(patient_entry.time,patient_dose.level,patient_DLT,patient_DLT.time,patient_Eff,patient_Eff.time)
}
patient_dataframe<-data.frame(patient_dataframe)
names(patient_dataframe)<-c("entry.time","dose.level","DLT","DLT.time","Eff","Eff.time")
follow_up_time<-function(x) min(follow_up,x)
patient_follow_up<- sapply(current_time-patient_dataframe$entry.time,follow_up_time)
patient_weights_tox<-patient_follow_up/follow_up
patient_weights_eff<-patient_follow_up/follow_up
#only DLT if we have seen it at the current time
current.DLT<-patient_dataframe$DLT
current.DLT[which(current.DLT==1)]<-patient_dataframe$DLT.time[which(current.DLT==1)]<=current_time
#only DLT time observed if current DLT is true
current.DLT.time<-patient_dataframe$DLT.time
current.DLT.time[which(current.DLT==0)]<-NA
#only Eff if we have seen it at the current time
current.Eff<-patient_dataframe$Eff
current.Eff[which(current.Eff==1)]<-patient_dataframe$Eff.time[which(current.Eff==1)]<=current_time
#only Eff time observed if current Eff is true
current.Eff.time<-patient_dataframe$Eff.time
current.Eff.time[which(current.Eff==0)]<-NA
entry.time<-patient_dataframe$entry.time
dose.level<-patient_dataframe$dose.level
patient_weights_tox[which(current.DLT==1)]<-1
patient_weights_eff[which(current.DLT==1)]<-(current.DLT.time[which(current.DLT==1)]-entry.time[which(current.DLT==1)])/follow_up
patient_weights_eff[which(current.Eff==1)]<-1
return(data.frame(entry.time,dose.level,current.DLT,current.DLT.time,current.Eff,current.Eff.time,patient_weights_tox,patient_weights_eff,patient_follow_up))
}
#create hard safety matrix
hard.safety.mat.function<-function(perc,co.size,max.cohorts){
hard.safety.mat<-matrix(nrow=2,ncol=max.cohorts)
hard.safety.mat[2,]<-seq(from=co.size,to=max.cohorts*co.size, by=co.size)
for(i in 1:max.cohorts){
numi<- hard.safety.mat[2,i]
try_vec<-c(1:numi)
probs_vec<-1-pbeta(0.3,1+try_vec,1+numi-try_vec)
hard.safety.mat[1,i]<- min( which(100*probs_vec>perc))
}
return(hard.safety.mat)
}
#function that takes the allowables list, the vector of doses assigned so far, and gives all allowable doses
allowable_func<-function(allowable.list.in,doses.so.far){
all.allowable<-unique(unlist(allowable.list.in[doses.so.far]))
return(all.allowable)
}
##INPUT:
#seed=seed for reproducibility
#tru.E.pars=parameters for data generation of efficacy times
#tru.T.pars=parameters for data generation of DLT times
#tru.corET=correlation between DLT times and efficacy times in data generation
#co_size=cohort size
#ncohorts=maximum number of cohorts in study
#target=target toxicity (all cycles)
#ncycles=number of cycles
#dose.skipping.rule= type of dose.skipping rule:
#"OFF" is no restrictions
#"ON" is once dose(a,b) is explored, all doses(x,y) such that x<=a+1 and y<=b are allowable, and x<=a and y<=b+1
#"ON.diag.allowed" is same as "ON" but also allows (a+1,b+1)
#prior_vec1: prior hyperparameters for 1 parameter independent model
#prior_vec1[1]: mean for toxicity
#prior_vec1[2]: precision for toxicity
#prior_vec1[3]: mean for efficacy
#prior_vec1[4]: precision for efficacy
#prior_vec2: prior hyperparameters for joint 2-parameter model
#mu_alphaT=prior_vec2[1] prior mean for a_T (intercept, toxicity)
#tau_alphaT=prior_vec2[2] prior precision for a_T (intercept, toxicity)
#mu_betaT=prior_vec2[3] prior mean for b_T (slope, toxicity)
#tau_betaT=prior_vec2[4] prior precision for b_T (slope, toxicity)
#mu_alphaE=prior_vec2[5] prior mean for a_E (intercept, efficacy)
#tau_alphaE=prior_vec2[6] prior precision for a_E (intercept, efficacy)
#mu_betaE=prior_vec2[7] prior mean for b_E (slope, efficacy)
#tau_betaE=prior_vec2[8] prior precision for b_E (slope, efficacy)
#mu_phi=prior_vec2[9] prior mean for phi (Gumbel model)
#tau_phi=prior_vec2[10] prior precision for phi (Gumbel model)
#sufficient.information==enforce stopping for sufficient information? no more than 9 patients per dose. T=enforce stopping for sufficient information (default)
#sufficient.information.lim= number of patients needed before stopping when the next assignment is the same.
#hard.safety.rule=percentage for hard safety rule based on Beta(1.1)?
#e.g. : 85= 2/3,3/6,4/9. 90=2/3,4/6,5/9, 95=3/3,4/6,5/9, <50 means no hard safety enforced
#safety.stopping.low.unsafe= (T=stop when P(p1>0.3)>0.8 (cycle 1))
#safety.stopping.high.toosafe= (T= stop when P(pJ>0.3)>0.8 (cycle 1))
#initial.one.cycle: Is the initial period based on one cycle at a time? (F=wait until all cycles completed before next dose in initial period)
#w1 & w2 : weights for utility function
#uppertox: value above which we penalise toxicity in utility
#C_eff: Dose is "admissible in efficacy if P(P(efficacy)>effbound)>C_eff
#C_tox: Dose is "admissible in safety if P(P(DLT)<toxbound)>C_tox
#effbound: Dose is "admissible in efficacy if P(P(efficacy)>effbound)>C_eff
#toxbound: Dose is "admissible in safety if P(P(DLT)<toxbound)>C_tox
#backfill: should doses deemed safe be backfilled? (Default FALSE) *NOTE backfill only uses default order*
#backfill.num: How many cohorts to add as backfilling?
#TITE: Is TITE-CRM used? (Default=T, F implies CRM only and is only compatible with ncycles=1)
#dose.indices: matrix with 2 col and ndoses rows, of indices for doses (i.e row 2 tells us dose "2" is [2,1] on the grid)
#wmE: matrix of working model (skeletons) for efficacy
#wmT: matrix of working model (skeletons) for toxicity
#prior.o.T: prior probability of the orderings for toxicity
#prior.o.E: prior probability of the orderings for efficacy
#default.order: Order in which we escalate if we see no activity/toxicity
#start.dose: The starting dose, as defined in the default order
#pause: Recruitment paused if no admissible doses, to allow observation of current patients more cycles
#a.stop.bound: number of patients required before activity is allowed to stop trial
##OUTPUT:
# dose.rec: dose recommendation
# num.pat: number of patients
# dose.ass: number of cohorts per dose (vector)
# stop.code: stopping reason
#1= No admissible doses
#2= Precision (N/A for pocrm)
#3= Max patients
#4= Sufficient information
#5= Lowest dose fails hard safety
#6= Lowest dose unsafe
#7= Highest Dose too safe
# num.DLT: number of DLTS (total)
# DLT.mat: 2xnumdoses matrix. rows for grades 1=no DLT, 2=DLT. cols for doses.
# Eff.mat: 2xnumdoses matrix. rows for grades 1=noEff, 2=Eff. cols for doses.
# duration: total trial duration (until all recruited patients are fully observed)
# max_admissable max admissable dose at the end of the trial according to hard safety only (has hard safety eliminated any?)
# dosevec: Dose assignment of cohorts in sequence order
# DLT.vec: Binary sequence of DLT outcomes (all cycles)
# EFF.vec: Binary sequence of efficacy outcomes (all cycles)
# all.data: All of the trial data
JOINT.TITE.POCRM<-function(seed,tru.E.pars,tru.T.pars,tru.corET,co_size,ncohorts ,target,
ncycles,dose.skipping.rule, prior_vec1, prior_vec2,
sufficient.information=T,sufficient.information.lim=29,hard.safety.rule=95,safety.stopping.low.unsafe=T,
safety.stopping.high.toosafe=T,initial.one.cycle=T,
w1, w2, upper.tox,C_eff=0.2,C_tox=0.2,effbound=0.2,toxbound=0.3,
backfill=F,backfill.num=2,TITE=T,dose.indices,
wmE,wmT,prior.o.T,prior.o.E,default.order,start.dose,pause=T,
a.stop.bound=0){
set.seed(seed)
stop_vec<-rep(0,7)
ndoses<-nrow(dose.indices)
dosevec<-c()
patient_ID1<-1
current.time<-0
excluded<-rep(0,ndoses)
#check that the data generation inputs tally with the number of doses and cycles
if(!all(dim(tru.T.pars)==dim(tru.E.pars))){
stop("Incompatible truth matrices for toxicity and efficacy")
}
if(ndoses!=ncol(tru.T.pars)){
stop("Incompatable truth matrix with number of doses")
}
if((TITE==F)&(ncycles!=1)){
stop("Incompatable TITE and ncycles input: ncycles must equal 1 for TITE=F")
}
#check the orderings are compatible
if(ncol(wmE)!=nrow(dose.indices)|ncol(wmT)!=nrow(dose.indices)|nrow(wmE)!=length(prior.o.E)|nrow(wmT)!=length(prior.o.T)){
stop("Incompatable skeletons")
}
if(!dose.skipping.rule%in%c("ON","ON.diag.allowed","OFF")){
stop("Dose skipping rule undefined")
}
##definitions of allowable escalations for dose.skipping.rule
#define the matrix
dose.mat<-matrix(nrow=max(dose.indices[,1]),ncol=max(dose.indices[,2]))
for(dose_i in 1:nrow(dose.indices)){
dose.mat[dose.indices[dose_i,1],dose.indices[dose_i,2]]<-dose_i
}
#define the allowable.escalations list
if(dose.skipping.rule=="ON"){
allowable.escalations<-list()
for(dose_i in 1:nrow(dose.indices)){
dose_index<-dose.indices[dose_i,]
all.below<-which((dose.indices[,1]<=dose_index[1])&(dose.indices[,2]<=dose_index[2]))
a_plus_1<-which((dose.indices[,1]<=(dose_index[1]+1))&(dose.indices[,2]<=dose_index[2]))
b_plus_1<-which((dose.indices[,1]<=dose_index[1])&(dose.indices[,2]<=(dose_index[2])+1))
allowable.escalations[[dose_i]]<-unique(c(all.below,a_plus_1,b_plus_1))
}
}else if(dose.skipping.rule=="ON.diag.allowed"){
allowable.escalations<-list()
for(dose_i in 1:nrow(dose.indices)){
dose_index<-dose.indices[dose_i,]
allowable.escalations[[dose_i]]<-which((dose.indices[,1]<=(dose_index[1]+1))&(dose.indices[,2]<=(dose_index[2])+1))
}
}
#define all doses as admissable before any are dropped for safety
max_admissable<-ndoses
if(hard.safety.rule>50){
hard.safety<-T
hard.safety.mat<-hard.safety.mat.function(perc=hard.safety.rule,co.size = co_size,max.cohorts = ncohorts)
}else{
hard.safety<-F
}
expand_vec<-rep(0, ndoses)
dose_rec<-NA
nextdose<-start.dose
stop<-0
initial<-1
while(stop==0){
#DATA GENERATION
if(current.time==0){
#first cohort
all.data<-multiple_patient_generation_TTE(patient_ID1=patient_ID1,efficacy_pars =tru.E.pars,tox_pars = tru.T.pars,corET=tru.corET,ncycles=ncycles ,thenextdose=nextdose,
entry_time=current.time,num_patients = co_size)
current.time<-current.time+1
patient_ID1<-max(all.data$patient_ID)+1
current.data<-all.data[all.data$time_of<=current.time,]
dosevec[current.time]<-nextdose
}else{
#subsequent cohorts
#escalation
all.data<-rbind(all.data,multiple_patient_generation_TTE(patient_ID1=patient_ID1,efficacy_pars =tru.E.pars,tox_pars = tru.T.pars,corET=tru.corET,ncycles=ncycles ,thenextdose=nextdose,
entry_time=current.time,num_patients = co_size)
)
patient_ID1<-max(all.data$patient_ID)+1
if(backfill==T){
#expansion
if(nextdose>1){ #if there are doses below the next dose
below.doses<-c(1:(nextdose-1)) #which doses are below?
expand.below<- expand_vec[below.doses] #have the below doses been expanded?
# which.expand<-which[expand.below==0] #which doses should now be expanded?
for(expand.doses in below.doses){ #for each dose that needs expanding
if(expand.below[expand.doses]==0){
#generate data for 2 cohorts on the dose
all.data<-rbind(all.data,multiple_patient_generation_TTE(patient_ID1=patient_ID1,efficacy_pars =tru.E.pars,tox_pars = tru.T.pars,corET=tru.corET,ncycles=ncycles ,thenextdose=expand.doses,
entry_time=current.time,num_patients = backfill.num*co_size))
#update patient ID for next assignment
patient_ID1<-max(all.data$patient_ID)+1
#update the expansion vector to say this dose has been expanded
expand_vec[expand.doses]<-1
}
}
}
}
current.time<-current.time+1
current.data<-all.data[all.data$time_of<=current.time,]
dosevec[current.time]<-nextdose
}
# patient.data<-patient_data_frame(all.data)
current.patient.data<-current_patient_data_frame(current_time=current.time,patient.dataframe=all.data,follow_up=ncycles)
##TITE CRM has an initial period where we keep escalating until we see a DLT
if(initial==1){
#next dose is next in the default order
initial.dose.seq.def<-c()
initial.dose.seq<-current.patient.data$dose.level
for(i in 1:length(initial.dose.seq)){
initial.dose.seq.def[i]<-which(default.order==initial.dose.seq[i])
}
nextdose.def<-min(max(initial.dose.seq.def)+1,ndoses)
nextdose<-default.order[nextdose.def]
##sufficient information
if(sufficient.information==T){
npats_doses<-sapply(c(1:ndoses), function(x) sum(current.patient.data$dose.level==x))
num_ass<-tabulate(dosevec,nbins = ndoses) #number of assignments
if(npats_doses[nextdose]>=sufficient.information.lim){
stop<-4
stop_vec[4]<-1
dose_rec<-nextdose
break
}
}
if(initial.one.cycle==T){
if((sum(current.patient.data$current.DLT)>0)|(sum(current.patient.data$current.Eff)>0)){
initial<-0
}
}else{
for (cyc in 1:(ncycles-1)){
current.patient.data<-current_patient_data_frame(current_time=current.time,patient.dataframe=all.data,follow_up=ncycles)
current.time<-current.time+1
if((sum(current.patient.data$current.DLT)>0)|(sum(current.patient.data$current.Eff)>0)){
initial<-0
current.time<-current.time-1
}
}
}
}
if(initial==0){
#posterior
patdoses<-current.patient.data$dose.level
# print(patdoses)
if(TITE==T){
weightE<-current.patient.data$patient_weights_eff
weightT<-current.patient.data$patient_weights_tox
}else{
weightE<-rep(1,length(current.patient.data$patient_weights_eff))
weightT<-rep(1,length(current.patient.data$patient_weights_tox))
}
#formatting for gibbs sampler
#yOUT
#1= no E no T
#2= no E yes T
#3= yes E no T
#4= yes E yes T
y_E<-current.patient.data$current.Eff
y_T<-current.patient.data$current.DLT
########################################
##decisions##
###################################
######### 1 par ind
margE1<-margT1<-c()
for (kE in 1:length(prior.o.E)) { #to calculate the marginal for each ordering
margE1[kE]<-integrate(V.post.fun.ind.1par,lower=-20,upper=20,y=y_E,patdoses=patdoses,wm_skeleton=wmE[kE,],
weight=weightE,prior_hyp=prior_vec1[3:4])$value
# print(kE)
}# for kE
for (kT in 1:length(prior.o.T)) {
# browser()
margT1[kT]<-integrate(V.post.fun.ind.1par,lower=-20,upper=20,y=y_T,patdoses=patdoses,wm_skeleton=wmT[kT,],
weight=weightT,prior_hyp=prior_vec1[1:2])$value
#print(kT)
} # for kT
#browser()
#posterior
pordE1 <- (margE1 * prior.o.E)/sum(margE1 * prior.o.E)
pordT1 <- (margT1 * prior.o.T)/sum(margT1 * prior.o.T)
#maximise the posterior - which ordering?
ordE1.I<-which.is.max.func(pordE1)
ordT1.I<-which.is.max.func(pordT1)
ord1.I <- c(ordE1.I,ordT1.I)
#calculate posterior for that ordering
currentdata_pocrm1.I<-list(ndoses=ncol(wmE),weightE=weightE,weightT=weightT,yOUT=category_function(T_vec=y_T, E_vec=y_E),dosesE=wmE[ordE1.I,],
dosesT=wmT[ordT1.I,],patdoses=patdoses,
mu_alphaT=prior_vec2[1],mu_betaT=prior_vec2[3],
tau_alphaT=prior_vec2[2],tau_betaT=prior_vec2[4],
mu_alphaE=prior_vec2[5],mu_betaE=prior_vec2[7],
tau_alphaE=prior_vec2[6],tau_betaE=prior_vec2[8],
mu_phi=prior_vec2[9],tau_phi=prior_vec2[10])
#browser()
gibbs_out_pocrm1.I<-gibbs_sampler.crm.combo(data_list=currentdata_pocrm1.I,iter=10000,model_string=model.crm.joint.string)
#browser()
#choose next dose
#R value for the best ordering
rpredT1.I <- expit(mean(gibbs_out_pocrm1.I$alphaT)+wmT[ordT1.I,]*mean(gibbs_out_pocrm1.I$betaT))
rpredE1.I<- expit(mean(gibbs_out_pocrm1.I$alphaE)+wmE[ordE1.I,]*mean(gibbs_out_pocrm1.I$betaE))
#browser()
# choose the next dose level
#admissable doses
admiss<-probeff<-probtox<-c()
for(dose.level in 1:ndoses){
probeff[dose.level]<-mean(expit((gibbs_out_pocrm1.I$alphaE)+wmE[ordE1.I,dose.level]*(gibbs_out_pocrm1.I$betaE))>effbound)
probtox[dose.level]<-mean(expit((gibbs_out_pocrm1.I$alphaT)+wmT[ordT1.I,dose.level]*(gibbs_out_pocrm1.I$betaT))<toxbound)
admiss[dose.level]<- (probeff[dose.level]>C_eff)&(probtox[dose.level]>C_tox)
}
admiss.doses<-which(admiss)
if((dose.skipping.rule=="ON")|(dose.skipping.rule=="ON.diag.allowed")){
allowable<-allowable_func(allowable.list.in = allowable.escalations,doses.so.far = dosevec)
admiss.doses.only<-admiss.doses
admiss.doses<-admiss.doses[admiss.doses%in%allowable]
}
#browser()
utility1.I<-p.tox.est<-p.eff.est<-c()
for(dose.level in admiss.doses){
p.tox.est[dose.level]<-expit(mean(gibbs_out_pocrm1.I$alphaT)+wmT[ordT1.I,dose.level]*mean(gibbs_out_pocrm1.I$betaT))
p.eff.est[dose.level]<-expit(mean(gibbs_out_pocrm1.I$alphaE)+wmE[ordE1.I,dose.level]*mean(gibbs_out_pocrm1.I$betaE))
utility1.I[dose.level]<-p.eff.est[dose.level] -
w1* p.tox.est[dose.level] -
w2* p.tox.est[dose.level]*as.numeric( p.tox.est[dose.level]>upper.tox)
}
##choosing the next dose:
nextdose<-which.max(utility1.I)
utility<-utility1.I
# if(ordT1.J!=ordT2){
# browser()
# }
# browser()
##if no admissable doses
if(length(nextdose)==0){
#pause recruitment but allow current patients to be observed more
if(pause==T){
pause.time<-current.time
while((length(nextdose)==0)&(current.time<pause.time+ncycles-1)){
#browser()
current.time<-current.time+1
current.data<-all.data[all.data$time_of<=current.time,]
current.patient.data<-current_patient_data_frame(current_time=current.time,patient.dataframe=all.data,follow_up=ncycles)
#posterior
patdoses<-current.patient.data$dose.level
# print(patdoses)
if(TITE==T){
weightE<-current.patient.data$patient_weights_eff
weightT<-current.patient.data$patient_weights_tox
}else{
weightE<-rep(1,length(current.patient.data$patient_weights_eff))
weightT<-rep(1,length(current.patient.data$patient_weights_tox))
}
#formatting for gibbs sampler
#yOUT
#1= no E no T
#2= no E yes T
#3= yes E no T
#4= yes E yes T
y_E<-current.patient.data$current.Eff
y_T<-current.patient.data$current.DLT
########################################
##decisions##
###################################
######### 1 par ind
margE1<-margT1<-c()
for (kE in 1:length(prior.o.E)) { #to calculate the marginal for each ordering
margE1[kE]<-integrate(V.post.fun.ind.1par,lower=-20,upper=20,y=y_E,patdoses=patdoses,wm_skeleton=wmE[kE,],
weight=weightE,prior_hyp=prior_vec1[3:4])$value
# print(kE)
}# for kE
for (kT in 1:length(prior.o.T)) {
# browser()
margT1[kT]<-integrate(V.post.fun.ind.1par,lower=-20,upper=20,y=y_T,patdoses=patdoses,wm_skeleton=wmT[kT,],
weight=weightT,prior_hyp=prior_vec1[1:2])$value
#print(kT)
} # for kT
#browser()
#posterior
pordE1 <- (margE1 * prior.o.E)/sum(margE1 * prior.o.E)
pordT1 <- (margT1 * prior.o.T)/sum(margT1 * prior.o.T)
#maximise the posterior - which ordering?
ordE1.I<-which.is.max.func(pordE1)
ordT1.I<-which.is.max.func(pordT1)
ord1.I <- c(ordE1.I,ordT1.I)
#calculate posterior for that ordering
currentdata_pocrm1.I<-list(ndoses=ncol(wmE),weightE=weightE,weightT=weightT,yOUT=category_function(T_vec=y_T, E_vec=y_E),dosesE=wmE[ordE1.I,],
dosesT=wmT[ordT1.I,],patdoses=patdoses,
mu_alphaT=prior_vec2[1],mu_betaT=prior_vec2[3],
tau_alphaT=prior_vec2[2],tau_betaT=prior_vec2[4],
mu_alphaE=prior_vec2[5],mu_betaE=prior_vec2[7],
tau_alphaE=prior_vec2[6],tau_betaE=prior_vec2[8],
mu_phi=prior_vec2[9],tau_phi=prior_vec2[10])
#browser()
gibbs_out_pocrm1.I<-gibbs_sampler.crm.combo(data_list=currentdata_pocrm1.I,iter=10000,model_string=model.crm.joint.string)
#browser()
#choose next dose
#R value for the best ordering
rpredT1.I <- expit(mean(gibbs_out_pocrm1.I$alphaT)+wmT[ordT1.I,]*mean(gibbs_out_pocrm1.I$betaT))
rpredE1.I<- expit(mean(gibbs_out_pocrm1.I$alphaE)+wmE[ordE1.I,]*mean(gibbs_out_pocrm1.I$betaE))
#browser()
# choose the next dose level
#admissable doses
admiss<-probeff<-probtox<-c()
for(dose.level in 1:ndoses){
probeff[dose.level]<-mean(expit((gibbs_out_pocrm1.I$alphaE)+wmE[ordE1.I,dose.level]*(gibbs_out_pocrm1.I$betaE))>effbound)
probtox[dose.level]<-mean(expit((gibbs_out_pocrm1.I$alphaT)+wmT[ordT1.I,dose.level]*(gibbs_out_pocrm1.I$betaT))<toxbound)
admiss[dose.level]<- (probeff[dose.level]>C_eff)&(probtox[dose.level]>C_tox)
}
admiss.doses<-which(admiss)
if((dose.skipping.rule=="ON")|(dose.skipping.rule=="ON.diag.allowed")){
allowable<-allowable_func(allowable.list.in = allowable.escalations,doses.so.far = dosevec)
admiss.doses.only<-admiss.doses
admiss.doses<-admiss.doses[admiss.doses%in%allowable]
}
#browser()
utility1.I<-p.tox.est<-p.eff.est<-c()
for(dose.level in admiss.doses){
p.tox.est[dose.level]<-expit(mean(gibbs_out_pocrm1.I$alphaT)+wmT[ordT1.I,dose.level]*mean(gibbs_out_pocrm1.I$betaT))
p.eff.est[dose.level]<-expit(mean(gibbs_out_pocrm1.I$alphaE)+wmE[ordE1.I,dose.level]*mean(gibbs_out_pocrm1.I$betaE))
utility1.I[dose.level]<-p.eff.est[dose.level] -
w1* p.tox.est[dose.level] -
w2* p.tox.est[dose.level]*as.numeric( p.tox.est[dose.level]>upper.tox)
}
##choosing the next dose:
nextdose<-which.max(utility1.I)
utility<-utility1.I
#browser()
} #while
} #if pause==TRUE
if(length(nextdose)==0){
if(length(y_T)>=a.stop.bound){
stop<-1
stop_vec[1]<-1
dose_rec<-NA
break
}else{
#choose admissibility based on tox only
#admissable doses
admiss<-c()
for(dose.level in 1:ndoses){
admiss[dose.level]<-(probtox[dose.level]>C_tox)
}
admiss.doses<-which(admiss)
#dose skipping
if((dose.skipping.rule=="ON")|(dose.skipping.rule=="ON.diag.allowed")){
allowable<-allowable_func(allowable.list.in = allowable.escalations,doses.so.far = dosevec)
admiss.doses.only<-admiss.doses
admiss.doses<-admiss.doses[admiss.doses%in%allowable]
}
#browser()
utility1.I<-p.tox.est<-p.eff.est<-c()
for(dose.level in admiss.doses){
p.tox.est[dose.level]<-expit(mean(gibbs_out_pocrm1.I$alphaT)+wmT[ordT1.I,dose.level]*mean(gibbs_out_pocrm1.I$betaT))
p.eff.est[dose.level]<-expit(mean(gibbs_out_pocrm1.I$alphaE)+wmE[ordE1.I,dose.level]*mean(gibbs_out_pocrm1.I$betaE))
utility1.I[dose.level]<-p.eff.est[dose.level] -
w1* p.tox.est[dose.level] -
w2* p.tox.est[dose.level]*as.numeric( p.tox.est[dose.level]>upper.tox)
}
##choosing the next dose:
nextdose<-which.max(utility1.I)
utility<-utility1.I
# browser()
##if still no admissible, then stop
if(length(nextdose)==0){
stop<-1
stop_vec[1]<-1
dose_rec<-NA
break
}
}
}
}
npats_doses<-sapply(c(1:ndoses), function(x) sum(current.patient.data$dose.level==x))
#stopping rules
if(((safety.stopping.low.unsafe==T)&(npats_doses[1]>0))|((safety.stopping.high.toosafe==T)&(npats_doses[ndoses]>0))){
current.patient.data_cyc1<-current_patient_data_frame(current_time=current.time,patient.dataframe=all.data,follow_up=1)
weightE1<-weightT1<-c()
patdoses1<-current.patient.data_cyc1$dose.level
yOUT1<-category_function(T_vec=current.patient.data_cyc1$current.DLT,E_vec=current.patient.data_cyc1$current.Eff)
#
if(TITE==T){
for(obser in 1:nrow(current.patient.data_cyc1)){
weightE1[obser]<-current.patient.data_cyc1$patient_weights_eff[obser]
weightT1[obser]<-current.patient.data_cyc1$patient_weights_tox[obser]
}
}else{
weightE1<-rep(1,nrow(current.patient.data_cyc1))
weightT1<-rep(1,nrow(current.patient.data_cyc1))
}
#only cycle 1
currentdata_pocrm_1cyc<-list(ndoses=ncol(wmE),yOUT=yOUT1,dosesE=wmE[ordE1.I,],
dosesT=wmT[ordT1.I,],
patdoses=patdoses1,
mu_alphaT=prior_vec2[1],mu_betaT=prior_vec2[3],
tau_alphaT=prior_vec2[2],tau_betaT=prior_vec2[4],
mu_alphaE=prior_vec2[5],mu_betaE=prior_vec2[7],
tau_alphaE=prior_vec2[6],tau_betaE=prior_vec2[8],
mu_phi=prior_vec2[9],tau_phi=prior_vec2[10],weightE=weightE1,
weightT=weightT1)
gibbs_out1<-gibbs_sampler.crm.combo(data_list=currentdata_pocrm_1cyc,iter=5000,model_string=model.crm.joint.string)
if((safety.stopping.low.unsafe==T)&(npats_doses[1]>0)){
posterior1<- expit((gibbs_out1$alphaT)+wmT[ordT1.I,1]*(gibbs_out1$betaT))
posterior1<-posterior1[1:length(posterior1)]
cyc1_0.3g<-mean(posterior1>0.3,na.rm=T)
if(cyc1_0.3g>0.8){
stop<-6
stop_vec[6]<-1
nextdose<-NA
}
}
if((safety.stopping.high.toosafe==T)&(npats_doses[ndoses]>0)){
posteriorJ<-expit((gibbs_out1$alphaT)+wmT[ordT1.I,ncol(wmT)]*(gibbs_out1$betaT))
posteriorJ<-posteriorJ[1:length(posteriorJ)]
cycJ_0.3l<-mean(posteriorJ<0.3,na.rm=T)
if(cycJ_0.3l>0.8){
stop<-7
stop_vec[7]<-1
nextdose<-NA
}
}
}
#number of DLTs per dose level
nDLTs_doses<-sapply(c(1:ndoses),function(x) sum(current.data$DLT[(current.data$dose_level==x)&(current.data$patient>0)]))
nDLTs_doses_c1<-sapply(c(1:ndoses),function(x) sum(current.data$DLT[(current.data$dose_level==x)&(current.data$patient>0)&(current.data$cycle==1)]))
##hard safety
if(hard.safety==T){
explored<-c(1:ndoses)[npats_doses>0]
for(do in explored){
if(nDLTs_doses_c1[do]>=hard.safety.mat[1, which(hard.safety.mat[2,]==npats_doses[do])]){
min.over.indices.hold<-dose.indices[do,]
excluded.hold<-as.numeric((dose.indices[,1]>=min.over.indices.hold[1])&(dose.indices[,2]>=min.over.indices.hold[2]))
excluded<-excluded+excluded.hold
}
}
if(!is.na(nextdose)){
if(excluded[nextdose]>0){#if the next dose is in the exclusion zone
included.utility<-utility
included.utility[excluded>0]<--100
included.utility[-admiss.doses]<--100
nextdose<-which.max(included.utility)
}
}
if(all(excluded>0)){
stop<-5
stop_vec[5]<-1
dose_rec<-NA
nextdose<-NA
}