R/semiMarkov_varyHR.R
In HealthEconomicsHackathon/ceatutorial: What the Package Does (One Line, Title Case)
Defines functions
semiMarkov_varyHR
##########################################################################################
##========================================================================================
## START OF SEMIMARKOV_VARYHR FUNCTION
##========================================================================================
##########################################################################################
##========================================================================================
## THIS FUNCTION BUILDS A SEMI-MARKOV MULTI-STATE MODEL AND RETURNS THE ASSOCIATED STATE
## OCCUPANCY PROBABILITIES (PROBABILITIES OVER TIME OF BEING IN EACH STATE)
## IT ALLOWS THE TREATMENT EFFECT IN THE EXTRAPOLATION PERIOD TO VARY FROM THAT IN THE
## OBSERVED PERIOD. IT DOES THIS BY CHANGING THE HAZARD RATIO IN THE EXTRAPOLATION PERIOD.
## THIS AMENDED HR IS APPLIED TO THE ONE TREATMENT GROUP TO OBTAIN THE HAZARD FOR THE OTHER
## TREATMENT GROUP
## TREATMENT MUST BE GIVEN AS THE FIRST ARGUMENT FOR EACH TRANSITION FOR covs
## MODIFIBLE ARGUMENTS:
## ntrans NUMBER OF TRANSITIONS IN THE MODELLING
## ncovs NUMBER OF COVARIATE PARAMETERS IN THE MODEL (EXCLUDING INTERCEPT) FOR EACH
## TRANSITION I.E. ONE FOR EACH BINARY AND CONTINUOUS VARIABLE AND K-1 FOR
## EACH CATEGORICAL VARIABLE, WHERE K= NO OF CATEGORIES
## covs VARIABLE NAMES FOR THE COVARIATES
## coveval VALUE AT WHICH TO EVALUATE EACH COVARIATE
## dist DISTRIBUTION TO USE FOR EACH TRANSITION. OPTIONS ARE:
## wei FOR WEIBULL, exp FOR EXPONENTIAL, gom FOR GOMPERTZ,
## logl FOR LOGLOGISTIC,logn FOR LOGNORMAL AND gam FOR GENERALISED GAMMA.
## IF FITTING THE SAME MODEL OVER THE OBSERVED AND EXTRAPOLATED PERIOD
## DISTRIBUTION WILL SPAN THE WHOLE TIME HORIZON. IF FITTING A DIFFERENT
## MODEL OVER THE OBSERVED PERIOD THAN FOR THE EXTRAPOLATION, DISTRIBUTION
## WILL SPAN THE OBSERVED PERIOD ONLY.
## dist2 ONLY APPLICABLE IF FITTING A DIFFERENT MODEL OVER THE EXTRAPOLATION PERIOD
## THAN THE OBSERVED PERIOD.DISTRIBUTION TO USE FOR EACH TRANSITION OVER THE
## EXTRAPOLATION PERIOD. OPTIONS ARE:
## wei FOR WEIBULL, exp FOR EXPONENTIAL, gom FOR GOMPERTZ,
## logl FOR LOGLOGISTIC,logn FOR LOGNORMAL AND gam FOR GENERALISED GAMMA.
## timeseq THE TIME POINTS TO USE FOR PREDICTIONS OVER THE OBSERVED PERIOD OF THE STUDY.
## THE FIRST ARGUMENT OF seq SHOULD BE THE START TIME, THE SECOND ARGUMENT THE
## END TIME AND THE THIRD ARGUMENT THE TIME INCREMENT.
## timeseq_ext THE TIME POINTS TO USE FOR PREDICTIONS OVER THE EXTRAPOLATION PERIOD.
## THE FIRST ARGUMENT OF seq SHOULD BE THE START TIME, THE SECOND ARGUMENT THE
## END TIME AND THE THIRD ARGUMENT THE TIME INCREMENT.
## data DATASET TO USE FOR MODELLING
## seedno NUMBER TO USE TO SET THE RANDOM NUMBER GENERATOR SO THAT SIMULATIONS CAN
## BE REPLICATED EXACTLY. IF NOT REQUIRED SET TO NULL
## M NUMBER OF SIMULATIONS USED TO CALCULATE PROBABILITIES
## trans TRANSITION MATRIX
## predinitial PREDICT FROM INITIAL STATE? EITHER TRUE OR FALSE
## predfrom IF NOT PREDICTING FROM INITIAL STATE, NUMBER OF STATE IN WHICH TO START
## PREDICTION
## varyHR ALTER THE HAZARD RATIO SO IT IS DIFFERENT IN THE EXTRAPOLATION PERIOD THAN
## IN THE OBSERVED PERIOD?. EITHER TRUE OR FALSE.
## HRtreat ONLY APPLICABLE IF varyHR=TRUE. THE HAZARD RATIOS TO USE IN THE
## EXTRAPOLATION PERIOD FOR EACH TRANSITION
## adjust ONLY APPLICABLE IF varyHR=TRUE. NUMBER OF TIME POINTS TO ADJUST FOR EACH
## TRANSITION TO FIX PROBLEMS WITH THE GAP IN HAZARDS (DIMINISH TREATMENT EFFECT
## FOR THE NUMBER OF TIME POINTS GIVEN BEFORE STOPPING THE TREATMENT EFFECT
## COMPLETELY
##========================================================================================
semiMarkov_varyHR<-function(ntrans=3, ncovs=c(1,1,2),
covs=rbind("covariate1", "covariate1",c("covariate1", "covariate2")),
coveval=rbind(0,0,c(0,1)),
dist=cbind("wei", "wei", "wei"),
dist2=cbind(NA, NA, NA),
timeseq=seq(0,4,1/12),
timeseq_ext=c(seq(49/12,118/12,1/12), seq(118/12+1/144, 12, 1/144),
seq(12+1/600,15, 1/600)),
data=msmcancer, seedno=12345, M=100,
trans=tmat,predinitial=TRUE, predfrom=2,
varyHR=FALSE, HRtreat=c(1,1,1),adjust=c(0,0,1)){
#### set up required lists
models<-vector("list", ntrans)
models_ext<-vector("list", ntrans)
fmla<-vector("list", ntrans)
covars<-vector("list", ntrans)
datasub<-vector("list", ntrans)
basecaseHR<-rep(NA, ntrans)
inc<-rep(NA, ntrans)
lp<-vector("list", ntrans)
lp_ext<-vector("list", ntrans)
coeffs<-vector("list", ntrans)
coeffs_ext<-vector("list", ntrans)
lp2<-vector("list", ntrans)
lp3<-vector("list", ntrans)
coeffs2<-vector("list", ntrans)
coeffs3<-vector("list", ntrans)
coeffs4<-vector("list", ntrans)
x<-vector("list", ntrans)
cumHaz<-vector("list", ntrans)
cumHaz_ext<-vector("list", ntrans)
kappa<-vector("list", ntrans)
gamma<-vector("list", ntrans)
mu<-vector("list", ntrans)
sigma<-vector("list", ntrans)
z<-vector("list", ntrans)
u<-vector("list", ntrans)
#### create the timepoints
tt<-timeseq
tt2<-timeseq_ext
for (i in 1:ntrans) {
#### coefficients from modelling of each transition
covars[[i]]<-covs[i,1:ncovs[i]]
fmla[[i]]<-as.formula(paste("Surv(time,status)~ ",paste(covars[[i]],
collapse= "+")))
datasub[[i]]<-subset(data,trans==i)
x[[i]]<-coveval[i,1:ncovs[i]]
if (dist[i]=="wei") {
models[[i]]<-phreg(fmla[[i]],dist="weibull", data=datasub[[i]])$coeff
coeffs[[i]]<-models[[i]][1:ncovs[i]]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
#### cumulative hazards for each transition
cumHaz[[i]]<-exp(-exp(models[[i]][ncovs[i]+2])*models[[i]][ncovs[i]+1]+lp[[i]])*
tt^exp(models[[i]][ncovs[i]+2])
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="wei")|(is.na(dist2[i])==TRUE & dist[i] =="wei")) {
models_ext[[i]]<-phreg(fmla[[i]],dist="weibull", data=datasub[[i]])$coeff
coeffs_ext[[i]]<-models_ext[[i]][1:ncovs[i]]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
#### cumulative hazards for each transition
cumHaz_ext[[i]]<-exp(-exp(models_ext[[i]][ncovs[i]+2])*models_ext[[i]][ncovs[i]+1]+lp_ext[[i]])*
tt2^exp(models_ext[[i]][ncovs[i]+2])
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0) cumHaz_ext[[i]][1:adjust[i]]<-exp(-exp(models_ext[[i]][ncovs[i]+2])*models_ext[[i]][ncovs[i]+1]+lp3[[i]][1:adjust[i]])*
tt2[1:adjust[i]]^exp(models_ext[[i]][ncovs[i]+2])
}
}
if (dist[i]=="exp") {
models[[i]]<-phreg(fmla[[i]],dist="weibull", shape=1, data=datasub[[i]])$coeff
coeffs[[i]]<-models[[i]][1:ncovs[i]]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
#### cumulative hazards for each transition
cumHaz[[i]]<-exp(-models[[i]][ncovs[i]+1]+lp[[i]])*tt
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="exp")|(is.na(dist2[i])==TRUE & dist[i] =="exp")) {
models_ext[[i]]<-phreg(fmla[[i]],dist="weibull", shape=1, data=datasub[[i]])$coeff
coeffs_ext[[i]]<-models_ext[[i]][1:ncovs[i]]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
#### cumulative hazards for each transition
cumHaz_ext[[i]]<-exp(-models_ext[[i]][ncovs[i]+1]+lp_ext[[i]])*tt2
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0) cumHaz_ext[[i]][1:adjust[i]]<-exp(-models_ext[[i]][ncovs[i]+1]+lp3[[i]][1:adjust[i]])*tt2[1:adjust[i]]
}
}
if (dist[i]=="gom") {
models[[i]]<-phreg(fmla[[i]],dist="gompertz", param="rate", data=datasub[[i]])$coeff
coeffs[[i]]<-models[[i]][1:ncovs[i]]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
#### cumulative hazards for each transition
cumHaz[[i]]<-exp(models[[i]][ncovs[i]+2]+lp[[i]])*(1/models[[i]][ncovs[i]+1])*
(exp(models[[i]][ncovs[i]+1]*tt) -1)
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="gom")|(is.na(dist2[i])==TRUE & dist[i] =="gom")) {
models_ext[[i]]<-phreg(fmla[[i]],dist="gompertz", param="rate", data=datasub[[i]])$coeff
coeffs_ext[[i]]<-models_ext[[i]][1:ncovs[i]]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
#### cumulative hazards for each transition
cumHaz_ext[[i]]<-exp(models_ext[[i]][ncovs[i]+2]+lp_ext[[i]])*(1/models_ext[[i]][ncovs[i]+1])*
(exp(models_ext[[i]][ncovs[i]+1]*tt2) -1)
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0) cumHaz_ext[[i]][1:adjust[i]]<-exp(models_ext[[i]][ncovs[i]+2]+lp3[[i]][1:adjust[i]])*(1/models_ext[[i]][ncovs[i]+1])*
(exp(models_ext[[i]][ncovs[i]+1]*tt2[1:adjust[i]]) -1)
}
}
if (dist[i]=="logl") {
models[[i]]<-aftreg(fmla[[i]],dist="loglogistic", data=datasub[[i]])$coeff
coeffs[[i]]<-models[[i]][1:ncovs[i]]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
#### cumulative hazards for each transition
cumHaz[[i]]<- -log(1/(1+(exp(-(models[[i]][ncovs[i]+1]-lp[[i]]))*tt)^
(1/(exp(-models[[i]][ncovs[i]+2])))))
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="logl")|(is.na(dist2[i])==TRUE & dist[i] =="logl")) {
models_ext[[i]]<-aftreg(fmla[[i]],dist="loglogistic", data=datasub[[i]])$coeff
coeffs_ext[[i]]<-models_ext[[i]][1:ncovs[i]]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
#### cumulative hazards for each transition
cumHaz_ext[[i]]<--log(1/(1+(exp(-(models_ext[[i]][ncovs[i]+1]-lp_ext[[i]]))*tt2)^
(1/(exp(-models_ext[[i]][ncovs[i]+2])))))
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0) cumHaz_ext[[i]][1:adjust[i]]<--log(1/(1+(exp(-(models_ext[[i]][ncovs[i]+1]-lp3[[i]][1:adjust[i]]))*tt2[1:adjust[i]])^
(1/(exp(-models_ext[[i]][ncovs[i]+2])))))
}
}
if (dist[i]=="logn") {
models[[i]]<-aftreg(fmla[[i]],dist="lognormal", data=datasub[[i]])$coeff
coeffs[[i]]<-models[[i]][1:ncovs[i]]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
#### cumulative hazards for each transition
cumHaz[[i]]<- -log(1-pnorm((log(tt)-(models[[i]][ncovs[i]+1]-lp[[i]]))/
(exp(-models[[i]][ncovs[i]+2]))))
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="logn")|(is.na(dist2[i])==TRUE & dist[i] =="logn")) {
models_ext[[i]]<-aftreg(fmla[[i]],dist="lognormal", data=datasub[[i]])$coeff
coeffs_ext[[i]]<-models_ext[[i]][1:ncovs[i]]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
#### cumulative hazards for each transition
cumHaz_ext[[i]]<--log(1-pnorm((log(tt2)-(models_ext[[i]][ncovs[i]+1]-lp_ext[[i]]))/
(exp(-models_ext[[i]][ncovs[i]+2]))))
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0) cumHaz_ext[[i]][1:adjust[i]]<--log(1-pnorm((log(tt2[1:adjust[i]])-(models_ext[[i]][ncovs[i]+1]-lp3[[i]][1:adjust[i]]))/
(exp(-models_ext[[i]][ncovs[i]+2]))))
}
}
if (dist[i]=="gam") {
models[[i]]<-flexsurvreg(fmla[[i]],dist="gengamma",data=datasub[[i]])$res
kappa[[i]]<- models[[i]][3,1]
gamma[[i]]<-(abs(kappa[[i]]))^(-2)
coeffs[[i]]<-models[[i]][4:(ncovs[i]+3),1]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
mu[[i]]<- models[[i]][1,1] +lp[[i]]
sigma[[i]]<- models[[i]][2,1]
z[[i]]<-rep(0,length(tt))
z[[i]]<- sign(kappa[[i]])*((log(tt)-mu[[i]])/sigma[[i]])
u[[i]]<-gamma[[i]]*exp((abs(kappa[[i]]))*z[[i]])
if(kappa[[i]]>0){
cumHaz[[i]]<--log(1-pgamma(u[[i]],gamma[[i]]))
}
if(kappa[[i]]==0){
cumHaz[[i]]<--log(1-pnorm(z[[i]]))
}
if(kappa[[i]]<0){
cumHaz[[i]]<--log(pgamma(u[[i]],gamma[[i]]))
}
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="gam")|(is.na(dist2[i])==TRUE & dist[i] =="gam")) {
models_ext[[i]]<-flexsurvreg(fmla[[i]],dist="gengamma",data=datasub[[i]])$res
kappa[[i]]<- models_ext[[i]][3,1]
gamma[[i]]<-(abs(kappa[[i]]))^(-2)
coeffs_ext[[i]]<-models_ext[[i]][4:(ncovs[i]+3),1]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
mu[[i]]<- models_ext[[i]][1,1] +lp_ext[[i]]
sigma[[i]]<- models_ext[[i]][2,1]
z[[i]]<-rep(0,length(tt2))
z[[i]]<- sign(kappa[[i]])*((log(tt2)-mu[[i]])/sigma[[i]])
u[[i]]<-gamma[[i]]*exp((abs(kappa[[i]]))*z[[i]])
if(kappa[[i]]>0){
cumHaz_ext[[i]]<--log(1-pgamma(u[[i]],gamma[[i]]))
}
if(kappa[[i]]==0){
cumHaz_ext[[i]]<--log(1-pnorm(z[[i]]))
}
if(kappa[[i]]<0){
cumHaz_ext[[i]]<--log(pgamma(u[[i]],gamma[[i]]))
}
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][4,1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0){
mu[[i]][1:adjust[i]]<- models_ext[[i]][1,1] +lp3[[i]] [1:adjust[i]]
sigma[[i]]<- models_ext[[i]][2,1]
z[[i]][1:adjust[i]]<-rep(0,length(tt2[1:adjust[i]]))
z[[i]][1:adjust[i]]<- sign(kappa[[i]])*((log(tt2[1:adjust[i]])-mu[[i]][1:adjust[i]])/sigma[[i]])
u[[i]][1:adjust[i]]<-gamma[[i]]*exp((abs(kappa[[i]]))*z[[i]])
}
if(kappa[[i]]>0 & adjust[i]!=0){
cumHaz_ext[[i]][1:adjust[i]]<--log(1-pgamma(u[[i]][1:adjust[i]],gamma[[i]]))
}
if(kappa[[i]]==0 & adjust[i]!=0){
cumHaz_ext[[i]][1:adjust[i]]<--log(1-pnorm(z[[i]][1:adjust[i]]))
}
if(kappa[[i]]<0 & adjust[i]!=0 ){
cumHaz_ext[[i]][1:adjust[i]]<--log(pgamma(u[[i]][1:adjust[i]],gamma[[i]]))
}
}
}
cumHaz[[i]]<-c(cumHaz[[i]], cumHaz_ext[[i]])
}
Haz<-unlist(cumHaz)
if (varyHR==TRUE) tt<- c(timeseq,timeseq_ext)
tt<-c(timeseq,timeseq_ext)
newtrans<-rep(1:ntrans,each=length(tt))
time<-rep(tt,ntrans)
Haz<-cbind(time=as.vector(time),Haz=as.vector(Haz),trans=as.vector(newtrans))
Haz<-as.data.frame(Haz)
## state occupancy probabilities
set.seed(seedno)
if (predinitial==TRUE) {
stateprobs <- mssample(Haz=Haz,trans=trans,tvec=tt,clock="reset", M=M)
}
if (predinitial==FALSE) {
stateprobs<-mssample(Haz=Haz,trans=trans, tvec=tt,clock="reset", M=M,
history=list(state=predfrom,time=0,tstate=NULL))
}
return(stateprobs)
}
##########################################################################################
##========================================================================================
## END OF SEMIMARKOV_VARYHR FUNCTION
##========================================================================================
##########################################################################################
HealthEconomicsHackathon/ceatutorial documentation built on Nov. 7, 2019, 12:09 a.m.
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Defines functions semiMarkov_varyHR
##########################################################################################
##========================================================================================
## START OF SEMIMARKOV_VARYHR FUNCTION
##========================================================================================
##########################################################################################
##========================================================================================
## THIS FUNCTION BUILDS A SEMI-MARKOV MULTI-STATE MODEL AND RETURNS THE ASSOCIATED STATE
## OCCUPANCY PROBABILITIES (PROBABILITIES OVER TIME OF BEING IN EACH STATE)
## IT ALLOWS THE TREATMENT EFFECT IN THE EXTRAPOLATION PERIOD TO VARY FROM THAT IN THE
## OBSERVED PERIOD. IT DOES THIS BY CHANGING THE HAZARD RATIO IN THE EXTRAPOLATION PERIOD.
## THIS AMENDED HR IS APPLIED TO THE ONE TREATMENT GROUP TO OBTAIN THE HAZARD FOR THE OTHER
## TREATMENT GROUP
## TREATMENT MUST BE GIVEN AS THE FIRST ARGUMENT FOR EACH TRANSITION FOR covs
## MODIFIBLE ARGUMENTS:
## ntrans NUMBER OF TRANSITIONS IN THE MODELLING
## ncovs NUMBER OF COVARIATE PARAMETERS IN THE MODEL (EXCLUDING INTERCEPT) FOR EACH
## TRANSITION I.E. ONE FOR EACH BINARY AND CONTINUOUS VARIABLE AND K-1 FOR
## EACH CATEGORICAL VARIABLE, WHERE K= NO OF CATEGORIES
## covs VARIABLE NAMES FOR THE COVARIATES
## coveval VALUE AT WHICH TO EVALUATE EACH COVARIATE
## dist DISTRIBUTION TO USE FOR EACH TRANSITION. OPTIONS ARE:
## wei FOR WEIBULL, exp FOR EXPONENTIAL, gom FOR GOMPERTZ,
## logl FOR LOGLOGISTIC,logn FOR LOGNORMAL AND gam FOR GENERALISED GAMMA.
## IF FITTING THE SAME MODEL OVER THE OBSERVED AND EXTRAPOLATED PERIOD
## DISTRIBUTION WILL SPAN THE WHOLE TIME HORIZON. IF FITTING A DIFFERENT
## MODEL OVER THE OBSERVED PERIOD THAN FOR THE EXTRAPOLATION, DISTRIBUTION
## WILL SPAN THE OBSERVED PERIOD ONLY.
## dist2 ONLY APPLICABLE IF FITTING A DIFFERENT MODEL OVER THE EXTRAPOLATION PERIOD
## THAN THE OBSERVED PERIOD.DISTRIBUTION TO USE FOR EACH TRANSITION OVER THE
## EXTRAPOLATION PERIOD. OPTIONS ARE:
## wei FOR WEIBULL, exp FOR EXPONENTIAL, gom FOR GOMPERTZ,
## logl FOR LOGLOGISTIC,logn FOR LOGNORMAL AND gam FOR GENERALISED GAMMA.
## timeseq THE TIME POINTS TO USE FOR PREDICTIONS OVER THE OBSERVED PERIOD OF THE STUDY.
## THE FIRST ARGUMENT OF seq SHOULD BE THE START TIME, THE SECOND ARGUMENT THE
## END TIME AND THE THIRD ARGUMENT THE TIME INCREMENT.
## timeseq_ext THE TIME POINTS TO USE FOR PREDICTIONS OVER THE EXTRAPOLATION PERIOD.
## THE FIRST ARGUMENT OF seq SHOULD BE THE START TIME, THE SECOND ARGUMENT THE
## END TIME AND THE THIRD ARGUMENT THE TIME INCREMENT.
## data DATASET TO USE FOR MODELLING
## seedno NUMBER TO USE TO SET THE RANDOM NUMBER GENERATOR SO THAT SIMULATIONS CAN
## BE REPLICATED EXACTLY. IF NOT REQUIRED SET TO NULL
## M NUMBER OF SIMULATIONS USED TO CALCULATE PROBABILITIES
## trans TRANSITION MATRIX
## predinitial PREDICT FROM INITIAL STATE? EITHER TRUE OR FALSE
## predfrom IF NOT PREDICTING FROM INITIAL STATE, NUMBER OF STATE IN WHICH TO START
## PREDICTION
## varyHR ALTER THE HAZARD RATIO SO IT IS DIFFERENT IN THE EXTRAPOLATION PERIOD THAN
## IN THE OBSERVED PERIOD?. EITHER TRUE OR FALSE.
## HRtreat ONLY APPLICABLE IF varyHR=TRUE. THE HAZARD RATIOS TO USE IN THE
## EXTRAPOLATION PERIOD FOR EACH TRANSITION
## adjust ONLY APPLICABLE IF varyHR=TRUE. NUMBER OF TIME POINTS TO ADJUST FOR EACH
## TRANSITION TO FIX PROBLEMS WITH THE GAP IN HAZARDS (DIMINISH TREATMENT EFFECT
## FOR THE NUMBER OF TIME POINTS GIVEN BEFORE STOPPING THE TREATMENT EFFECT
## COMPLETELY
##========================================================================================
semiMarkov_varyHR<-function(ntrans=3, ncovs=c(1,1,2),
covs=rbind("covariate1", "covariate1",c("covariate1", "covariate2")),
coveval=rbind(0,0,c(0,1)),
dist=cbind("wei", "wei", "wei"),
dist2=cbind(NA, NA, NA),
timeseq=seq(0,4,1/12),
timeseq_ext=c(seq(49/12,118/12,1/12), seq(118/12+1/144, 12, 1/144),
seq(12+1/600,15, 1/600)),
data=msmcancer, seedno=12345, M=100,
trans=tmat,predinitial=TRUE, predfrom=2,
varyHR=FALSE, HRtreat=c(1,1,1),adjust=c(0,0,1)){
#### set up required lists
models<-vector("list", ntrans)
models_ext<-vector("list", ntrans)
fmla<-vector("list", ntrans)
covars<-vector("list", ntrans)
datasub<-vector("list", ntrans)
basecaseHR<-rep(NA, ntrans)
inc<-rep(NA, ntrans)
lp<-vector("list", ntrans)
lp_ext<-vector("list", ntrans)
coeffs<-vector("list", ntrans)
coeffs_ext<-vector("list", ntrans)
lp2<-vector("list", ntrans)
lp3<-vector("list", ntrans)
coeffs2<-vector("list", ntrans)
coeffs3<-vector("list", ntrans)
coeffs4<-vector("list", ntrans)
x<-vector("list", ntrans)
cumHaz<-vector("list", ntrans)
cumHaz_ext<-vector("list", ntrans)
kappa<-vector("list", ntrans)
gamma<-vector("list", ntrans)
mu<-vector("list", ntrans)
sigma<-vector("list", ntrans)
z<-vector("list", ntrans)
u<-vector("list", ntrans)
#### create the timepoints
tt<-timeseq
tt2<-timeseq_ext
for (i in 1:ntrans) {
#### coefficients from modelling of each transition
covars[[i]]<-covs[i,1:ncovs[i]]
fmla[[i]]<-as.formula(paste("Surv(time,status)~ ",paste(covars[[i]],
collapse= "+")))
datasub[[i]]<-subset(data,trans==i)
x[[i]]<-coveval[i,1:ncovs[i]]
if (dist[i]=="wei") {
models[[i]]<-phreg(fmla[[i]],dist="weibull", data=datasub[[i]])$coeff
coeffs[[i]]<-models[[i]][1:ncovs[i]]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
#### cumulative hazards for each transition
cumHaz[[i]]<-exp(-exp(models[[i]][ncovs[i]+2])*models[[i]][ncovs[i]+1]+lp[[i]])*
tt^exp(models[[i]][ncovs[i]+2])
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="wei")|(is.na(dist2[i])==TRUE & dist[i] =="wei")) {
models_ext[[i]]<-phreg(fmla[[i]],dist="weibull", data=datasub[[i]])$coeff
coeffs_ext[[i]]<-models_ext[[i]][1:ncovs[i]]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
#### cumulative hazards for each transition
cumHaz_ext[[i]]<-exp(-exp(models_ext[[i]][ncovs[i]+2])*models_ext[[i]][ncovs[i]+1]+lp_ext[[i]])*
tt2^exp(models_ext[[i]][ncovs[i]+2])
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0) cumHaz_ext[[i]][1:adjust[i]]<-exp(-exp(models_ext[[i]][ncovs[i]+2])*models_ext[[i]][ncovs[i]+1]+lp3[[i]][1:adjust[i]])*
tt2[1:adjust[i]]^exp(models_ext[[i]][ncovs[i]+2])
}
}
if (dist[i]=="exp") {
models[[i]]<-phreg(fmla[[i]],dist="weibull", shape=1, data=datasub[[i]])$coeff
coeffs[[i]]<-models[[i]][1:ncovs[i]]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
#### cumulative hazards for each transition
cumHaz[[i]]<-exp(-models[[i]][ncovs[i]+1]+lp[[i]])*tt
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="exp")|(is.na(dist2[i])==TRUE & dist[i] =="exp")) {
models_ext[[i]]<-phreg(fmla[[i]],dist="weibull", shape=1, data=datasub[[i]])$coeff
coeffs_ext[[i]]<-models_ext[[i]][1:ncovs[i]]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
#### cumulative hazards for each transition
cumHaz_ext[[i]]<-exp(-models_ext[[i]][ncovs[i]+1]+lp_ext[[i]])*tt2
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0) cumHaz_ext[[i]][1:adjust[i]]<-exp(-models_ext[[i]][ncovs[i]+1]+lp3[[i]][1:adjust[i]])*tt2[1:adjust[i]]
}
}
if (dist[i]=="gom") {
models[[i]]<-phreg(fmla[[i]],dist="gompertz", param="rate", data=datasub[[i]])$coeff
coeffs[[i]]<-models[[i]][1:ncovs[i]]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
#### cumulative hazards for each transition
cumHaz[[i]]<-exp(models[[i]][ncovs[i]+2]+lp[[i]])*(1/models[[i]][ncovs[i]+1])*
(exp(models[[i]][ncovs[i]+1]*tt) -1)
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="gom")|(is.na(dist2[i])==TRUE & dist[i] =="gom")) {
models_ext[[i]]<-phreg(fmla[[i]],dist="gompertz", param="rate", data=datasub[[i]])$coeff
coeffs_ext[[i]]<-models_ext[[i]][1:ncovs[i]]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
#### cumulative hazards for each transition
cumHaz_ext[[i]]<-exp(models_ext[[i]][ncovs[i]+2]+lp_ext[[i]])*(1/models_ext[[i]][ncovs[i]+1])*
(exp(models_ext[[i]][ncovs[i]+1]*tt2) -1)
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0) cumHaz_ext[[i]][1:adjust[i]]<-exp(models_ext[[i]][ncovs[i]+2]+lp3[[i]][1:adjust[i]])*(1/models_ext[[i]][ncovs[i]+1])*
(exp(models_ext[[i]][ncovs[i]+1]*tt2[1:adjust[i]]) -1)
}
}
if (dist[i]=="logl") {
models[[i]]<-aftreg(fmla[[i]],dist="loglogistic", data=datasub[[i]])$coeff
coeffs[[i]]<-models[[i]][1:ncovs[i]]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
#### cumulative hazards for each transition
cumHaz[[i]]<- -log(1/(1+(exp(-(models[[i]][ncovs[i]+1]-lp[[i]]))*tt)^
(1/(exp(-models[[i]][ncovs[i]+2])))))
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="logl")|(is.na(dist2[i])==TRUE & dist[i] =="logl")) {
models_ext[[i]]<-aftreg(fmla[[i]],dist="loglogistic", data=datasub[[i]])$coeff
coeffs_ext[[i]]<-models_ext[[i]][1:ncovs[i]]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
#### cumulative hazards for each transition
cumHaz_ext[[i]]<--log(1/(1+(exp(-(models_ext[[i]][ncovs[i]+1]-lp_ext[[i]]))*tt2)^
(1/(exp(-models_ext[[i]][ncovs[i]+2])))))
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0) cumHaz_ext[[i]][1:adjust[i]]<--log(1/(1+(exp(-(models_ext[[i]][ncovs[i]+1]-lp3[[i]][1:adjust[i]]))*tt2[1:adjust[i]])^
(1/(exp(-models_ext[[i]][ncovs[i]+2])))))
}
}
if (dist[i]=="logn") {
models[[i]]<-aftreg(fmla[[i]],dist="lognormal", data=datasub[[i]])$coeff
coeffs[[i]]<-models[[i]][1:ncovs[i]]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
#### cumulative hazards for each transition
cumHaz[[i]]<- -log(1-pnorm((log(tt)-(models[[i]][ncovs[i]+1]-lp[[i]]))/
(exp(-models[[i]][ncovs[i]+2]))))
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="logn")|(is.na(dist2[i])==TRUE & dist[i] =="logn")) {
models_ext[[i]]<-aftreg(fmla[[i]],dist="lognormal", data=datasub[[i]])$coeff
coeffs_ext[[i]]<-models_ext[[i]][1:ncovs[i]]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
#### cumulative hazards for each transition
cumHaz_ext[[i]]<--log(1-pnorm((log(tt2)-(models_ext[[i]][ncovs[i]+1]-lp_ext[[i]]))/
(exp(-models_ext[[i]][ncovs[i]+2]))))
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0) cumHaz_ext[[i]][1:adjust[i]]<--log(1-pnorm((log(tt2[1:adjust[i]])-(models_ext[[i]][ncovs[i]+1]-lp3[[i]][1:adjust[i]]))/
(exp(-models_ext[[i]][ncovs[i]+2]))))
}
}
if (dist[i]=="gam") {
models[[i]]<-flexsurvreg(fmla[[i]],dist="gengamma",data=datasub[[i]])$res
kappa[[i]]<- models[[i]][3,1]
gamma[[i]]<-(abs(kappa[[i]]))^(-2)
coeffs[[i]]<-models[[i]][4:(ncovs[i]+3),1]
lp[[i]]<-sum(coeffs[[i]]* x[[i]] )
mu[[i]]<- models[[i]][1,1] +lp[[i]]
sigma[[i]]<- models[[i]][2,1]
z[[i]]<-rep(0,length(tt))
z[[i]]<- sign(kappa[[i]])*((log(tt)-mu[[i]])/sigma[[i]])
u[[i]]<-gamma[[i]]*exp((abs(kappa[[i]]))*z[[i]])
if(kappa[[i]]>0){
cumHaz[[i]]<--log(1-pgamma(u[[i]],gamma[[i]]))
}
if(kappa[[i]]==0){
cumHaz[[i]]<--log(1-pnorm(z[[i]]))
}
if(kappa[[i]]<0){
cumHaz[[i]]<--log(pgamma(u[[i]],gamma[[i]]))
}
}
if ((is.na(dist2[i])==FALSE & dist2[i] =="gam")|(is.na(dist2[i])==TRUE & dist[i] =="gam")) {
models_ext[[i]]<-flexsurvreg(fmla[[i]],dist="gengamma",data=datasub[[i]])$res
kappa[[i]]<- models_ext[[i]][3,1]
gamma[[i]]<-(abs(kappa[[i]]))^(-2)
coeffs_ext[[i]]<-models_ext[[i]][4:(ncovs[i]+3),1]
if (varyHR==FALSE){
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
if (varyHR==TRUE){
coeffs_ext[[i]][1]<-log(HRtreat[i])
lp_ext[[i]]<-sum(coeffs_ext[[i]]* x[[i]] )}
mu[[i]]<- models_ext[[i]][1,1] +lp_ext[[i]]
sigma[[i]]<- models_ext[[i]][2,1]
z[[i]]<-rep(0,length(tt2))
z[[i]]<- sign(kappa[[i]])*((log(tt2)-mu[[i]])/sigma[[i]])
u[[i]]<-gamma[[i]]*exp((abs(kappa[[i]]))*z[[i]])
if(kappa[[i]]>0){
cumHaz_ext[[i]]<--log(1-pgamma(u[[i]],gamma[[i]]))
}
if(kappa[[i]]==0){
cumHaz_ext[[i]]<--log(1-pnorm(z[[i]]))
}
if(kappa[[i]]<0){
cumHaz_ext[[i]]<--log(pgamma(u[[i]],gamma[[i]]))
}
if (varyHR==TRUE){
basecaseHR[i]<-exp(models_ext[[i]][4,1])
if (adjust[i]!=0) inc[i]<-(basecaseHR[i]-HRtreat[i])/(adjust[i]+1)
if (adjust[i]!=0) coeffs3[[i]][1:adjust[i]]<- log(seq(basecaseHR[i]-inc[i],HRtreat[i], -inc[i]))
#coeffs4[[i]]<-coeffs2[[i]]
if (adjust[i]!=0) { for (j in 1:adjust[i]) {
coeffs4[[i]][[j]]<-coeffs_ext[[i]]
coeffs4[[i]][[j]][1]<-coeffs3[[i]][j]
lp3[[i]][j]<-sum(coeffs4[[i]][[j]]* x[[i]])
}}
if (adjust[i]==0) lp3[[i]]<-lp_ext[[i]]
if (adjust[i]!=0){
mu[[i]][1:adjust[i]]<- models_ext[[i]][1,1] +lp3[[i]] [1:adjust[i]]
sigma[[i]]<- models_ext[[i]][2,1]
z[[i]][1:adjust[i]]<-rep(0,length(tt2[1:adjust[i]]))
z[[i]][1:adjust[i]]<- sign(kappa[[i]])*((log(tt2[1:adjust[i]])-mu[[i]][1:adjust[i]])/sigma[[i]])
u[[i]][1:adjust[i]]<-gamma[[i]]*exp((abs(kappa[[i]]))*z[[i]])
}
if(kappa[[i]]>0 & adjust[i]!=0){
cumHaz_ext[[i]][1:adjust[i]]<--log(1-pgamma(u[[i]][1:adjust[i]],gamma[[i]]))
}
if(kappa[[i]]==0 & adjust[i]!=0){
cumHaz_ext[[i]][1:adjust[i]]<--log(1-pnorm(z[[i]][1:adjust[i]]))
}
if(kappa[[i]]<0 & adjust[i]!=0 ){
cumHaz_ext[[i]][1:adjust[i]]<--log(pgamma(u[[i]][1:adjust[i]],gamma[[i]]))
}
}
}
cumHaz[[i]]<-c(cumHaz[[i]], cumHaz_ext[[i]])
}
Haz<-unlist(cumHaz)
if (varyHR==TRUE) tt<- c(timeseq,timeseq_ext)
tt<-c(timeseq,timeseq_ext)
newtrans<-rep(1:ntrans,each=length(tt))
time<-rep(tt,ntrans)
Haz<-cbind(time=as.vector(time),Haz=as.vector(Haz),trans=as.vector(newtrans))
Haz<-as.data.frame(Haz)
## state occupancy probabilities
set.seed(seedno)
if (predinitial==TRUE) {
stateprobs <- mssample(Haz=Haz,trans=trans,tvec=tt,clock="reset", M=M)
}
if (predinitial==FALSE) {
stateprobs<-mssample(Haz=Haz,trans=trans, tvec=tt,clock="reset", M=M,
history=list(state=predfrom,time=0,tstate=NULL))
}
return(stateprobs)
}
##########################################################################################
##========================================================================================
## END OF SEMIMARKOV_VARYHR FUNCTION
##========================================================================================
##########################################################################################
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