Function for bootstrapped confidence interval

Description

Function for bootstrapped confidence interval

Usage

bootciJMCS(object, future_time)

Arguments

Value

Returns bootstraped confidence interval for model fitted through FastJM

Joint model for Bidirectional survival data using JMbayes2

Description

The function fits joint model for survival data with two events. It utilizes the JMbayes2 package for obtaining the model parameter estimates.

Usage

jmbB(
  dtlong,
  dtsurv,
  longm,
  survm,
  rd,
  timeVar,
  id,
  samplesize = NULL,
  BIGdata = FALSE,
  niter = 200,
  nburnin = 100,
  nchain = 1
)

Arguments

Value

Estimated model parameters of Joint model with bidirectional survival data

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma

References

Rizopoulos, D., G. Papageorgiou, and P. Miranda Afonso. "JMbayes2: extended joint models for longitudinal and time-to-event data." R package version 0.2-4 (2022).

Bhattacharjee, A., Rajbongshi, B. K., & Vishwakarma, G. K. (2024). jmBIG: enhancing dynamic risk prediction and personalized medicine through joint modeling of longitudinal and survival data in big routinely collected data. BMC Medical Research Methodology, 24(1), 172.

Examples

library(JMbayes2)
st_pbcid<-function(){
  new_pbcid<-pbc2.id
  new_pbcid$time_2<-rexp(n=nrow(pbc2.id),1/10)
  cen_time<-runif(nrow(pbc2.id),min(new_pbcid$time_2),max(new_pbcid$time_2))
  status_2<-ifelse(new_pbcid$time_2<cen_time,1,0)
  new_pbcid$status_2<-status_2
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<cen_time,new_pbcid$time_2,cen_time)
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<new_pbcid$years,new_pbcid$years,new_pbcid$time_2)
  new_pbcid
}
new_pbc2id<-st_pbcid()
pbc2$status_2<-rep(new_pbc2id$status_2,times=data.frame(table(pbc2$id))$Freq)
pbc2$time_2<-rep(new_pbc2id$time_2,times=data.frame(table(pbc2$id))$Freq)
pbc2_new<-pbc2[pbc2$id%in%c(1:100),]
new_pbc2id<-new_pbc2id[new_pbc2id$id%in%c(1:100),]
model_jmbBdirect<-jmbB(dtlong=pbc2_new,dtsurv =new_pbc2id,
                       longm=list(serBilir~drug*year,serBilir~drug*year),
                       survm=list(Surv(years,status2)~drug,Surv(time_2,status_2)~drug+age),
                       rd=list(~year|id,~year|id),
                       id='id',timeVar ='year')
model_jmbBdirect

Joint model for Bidirectional survival data using FastJM

Description

The function fits joint model for survival data with two events. It utilizes the FastJM package for obtaining the model parameter estimates.

Usage

jmcsB(
  dtlong,
  dtsurv,
  longm,
  survm,
  rd,
  id,
  timeVar,
  BIGdata = FALSE,
  samplesize = 200
)

Arguments

Value

Estimated model parameters of Joint model with bidirectional survival data

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma

References

Li, Shanpeng, et al. "Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risk Data: With Applications to Massive Biobank Data." Computational and Mathematical Methods in Medicine 2022 (2022).

Bhattacharjee, A., Rajbongshi, B. K., & Vishwakarma, G. K. (2024). jmBIG: enhancing dynamic risk prediction and personalized medicine through joint modeling of longitudinal and survival data in big routinely collected data. BMC Medical Research Methodology, 24(1), 172.

Examples

library(FastJM)
library(JMbayes2)
st_pbcid<-function(){
  new_pbcid<-pbc2.id
  new_pbcid$time_2<-rexp(n=nrow(pbc2.id),1/10)
  cen_time<-runif(nrow(pbc2.id),min(new_pbcid$time_2),max(new_pbcid$time_2))
  status_2<-ifelse(new_pbcid$time_2<cen_time,1,0)
  new_pbcid$status_2<-status_2
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<cen_time,new_pbcid$time_2,cen_time)
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<new_pbcid$years,new_pbcid$years,new_pbcid$time_2)
  new_pbcid
}
new_pbc2id<-st_pbcid()
pbc2$status_2<-rep(new_pbc2id$status_2,times=data.frame(table(pbc2$id))$Freq)
pbc2$time_2<-rep(new_pbc2id$time_2,times=data.frame(table(pbc2$id))$Freq)
pbc2_new<-pbc2[pbc2$id%in%c(1:50),]
new_pbc2id<-new_pbc2id[new_pbc2id$id%in%c(1:50),]
model_jmcs<-jmcsB(dtlong=pbc2_new,dtsurv=new_pbc2id,
                  longm=list(serBilir~drug*year,
                             serBilir~drug*year),
                  survm=list(Surv(years,status2)~drug,
                             Surv(time_2,status_2)~drug+age),
                  rd=list(~1|id,~1|id),
                  id='id',timeVar='year')
model_jmcs

Joint model for Bidirectional survival data using joineRML

Description

The function fits joint model for survival data with two events. It utilizes the joineRML package for obtaining the model parameter estimates.

Usage

jmrmlB(
  dtlong,
  dtsurv,
  longm,
  survm,
  rd,
  timeVar,
  id,
  samplesize = 200,
  BIGdata = FALSE
)

Arguments

Value

Estimated model parameters of Joint model with bidirectional survival data

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma

References

Hickey, Graeme L., et al. "joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes." BMC medical research methodology 18 (2018): 1-14.

Bhattacharjee, A., Rajbongshi, B. K., & Vishwakarma, G. K. (2024). jmBIG: enhancing dynamic risk prediction and personalized medicine through joint modeling of longitudinal and survival data in big routinely collected data. BMC Medical Research Methodology, 24(1), 172.

Examples

 
##
library(JMbayes2)
library(joineRML)
jmrmlBModel<-jmrmlB(dtlong=new_long2[new_long2$id%in%c(1:80),],
                    dtsurv=new_surv2[new_surv2$id%in%c(1:80),],
                    longm=list(y~x7+visit,y~x7+visit),survm=list(Surv(time,status)~x1+visit,
                    Surv(time_2,status_2)~x1+visit),rd=list(~visit|id,~visit|id),id='id',
                    timeVar='visit',samplesize=40,BIGdata=TRUE)
jmrmlBModel
##

Joint model for Bidirectional survival data using rstanarm

Description

The function fits joint model for survival data with two events. It utilizes the rstanarm package for obtaining the model parameter estimates.

Usage

jmstB(
  dtlong,
  dtsurv,
  longm,
  survm,
  timeVar,
  id,
  nchain = 1,
  refresh = 1000,
  BIGdata = FALSE,
  samplesize = 200
)

Arguments

Value

Estimated model parameters of Joint model with bidirectional survival data

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma

References

Goodrich, B., et al. "rstanarm: Bayesian applied regression modeling via Stan. R package version 2.17. 4." Online< http://mc-stan. org (2018).

Bhattacharjee, A., Rajbongshi, B. K., & Vishwakarma, G. K. (2024). jmBIG: enhancing dynamic risk prediction and personalized medicine through joint modeling of longitudinal and survival data in big routinely collected data. BMC Medical Research Methodology, 24(1), 172.

Examples

 
 ##
library(JMbayes2)
library(rstanarm)
st_pbcid<-function(){
  new_pbcid<-pbc2.id
  new_pbcid$time_2<-rexp(n=nrow(pbc2.id),1/10)
  cen_time<-runif(nrow(pbc2.id),min(new_pbcid$time_2),max(new_pbcid$time_2))
  status_2<-ifelse(new_pbcid$time_2<cen_time,1,0)
  new_pbcid$status_2<-status_2
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<cen_time,new_pbcid$time_2,cen_time)
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<new_pbcid$years,new_pbcid$years,
                           new_pbcid$time_2)
  new_pbcid
}
new_pbc2id<-st_pbcid()
pbc2$status_2<-rep(new_pbc2id$status_2,times=data.frame(table(pbc2$id))$Freq)
pbc2$time_2<-rep(new_pbc2id$time_2,times=data.frame(table(pbc2$id))$Freq)
pbc2_new<-pbc2[pbc2$id%in%c(1:50),]
new_pbc2id<-new_pbc2id[new_pbc2id$id%in%c(1:50),]
model_jmstBdirect<-jmstB(
  dtlong=pbc2_new,
  dtsurv = new_pbc2id,
  longm=list(serBilir~drug*year+(year|id),albumin~drug+year+(year|id)),
  survm=list(Surv(years,status2)~drug,Surv(time_2,status_2)~drug),
  timeVar="year",
  id='id',
  refresh=400,
  nchain=1)
model_jmstBdirect
##
 

longitudinal- survival dataset

Description

A longitudinal dataset with single marker , with different numeric and categorical covariate

Usage

data(new_long2)

Format

a tibble of 13 columns and 5639 observations,

id

id value for subjects

status

survival status

time

survival time

y

longitudinal marker

visit

visit time of longitudinal measurements

x1,x2,...,x7

different numeric and categorical variable

survival data

Description

A survival dataset related the long2 dataset, with different numeric and categorical covariate

Usage

data(new_surv2)

Format

a tibble of 13 columns and 1000 observations,

id

id value for subjects

status

survival status

time

survival time

visit

visit time of longitudinal measurements

x1,x2,...,x7

different numeric and categorical variable

Prediction plot from jmbB()

Description

Prediction plot from jmbB()

Usage

## S3 method for class 'jmbB'
plot(x, y, ...)

Arguments

Value

Returns prediction plot for the newdata using the model fitted through jmbB().

Note

In the example code we use newdata as the data for ID 2 in the PBC2 dataset, it has follow up information till 8.832. Now suppose we want to look at the survival of ID 2 under joint model 1 after time 4 and for joint model 2 after time 9. For that we created the newdata as if the individual is followed till for a time period less than min(4,9).

Examples

 
##
library(JMbayes2)
st_pbcid<-function(){
  new_pbcid<-pbc2.id
  new_pbcid$time_2<-rexp(n=nrow(pbc2.id),1/10)
  cen_time<-runif(nrow(pbc2.id),min(new_pbcid$time_2),max(new_pbcid$time_2))
  status_2<-ifelse(new_pbcid$time_2<cen_time,1,0)
  new_pbcid$status_2<-status_2
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<cen_time,new_pbcid$time_2,cen_time)
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<new_pbcid$years,new_pbcid$years,new_pbcid$time_2)
  new_pbcid
}
new_pbc2id<-st_pbcid()
pbc2$status_2<-rep(new_pbc2id$status_2,times=data.frame(table(pbc2$id))$Freq)
pbc2$time_2<-rep(new_pbc2id$time_2,times=data.frame(table(pbc2$id))$Freq)
pbc2_new<-pbc2[pbc2$id%in%c(1:100),]
new_pbc2id<-new_pbc2id[new_pbc2id$id%in%c(1:100),]
model_jmbBdirect<-jmbB(dtlong=pbc2_new,dtsurv = new_pbc2id,
                       longm=list(serBilir ~ drug*year,serBilir ~ drug*year),
                       survm=list(Surv(years,status2) ~ drug,
                                  Surv(time_2,status_2) ~ drug+age),
                       rd=list(~year|id,~year|id),
                       id='id',timeVar ='year')

t0<-4
nd <- pbc2[pbc2$id %in% c(2), ]
nd<-nd[nd$year<t0,]
nd$status2<-0
nd$years<-t0
nd$time_2<-9
nd$status_2<-0
plot(model_jmbBdirect,nd)
nd <- pbc2[pbc2$id %in% c(2), ]
nd<-nd[nd$year<12,]
nd$status2<-0
nd$years<-12
nd$time_2<-9
nd$status_2<-0
plot(model_jmbBdirect,nd)
##
 

Prediction plot from jmcsB()

Description

Prediction plot from jmcsB()

Usage

## S3 method for class 'jmcsB'
plot(x, y, ...)

Arguments

Value

Returns prediction plot for the newdata using the model fitted through jmcsB()

Note

In the example code we use newdata as the data for ID 2 in the PBC2 dataset, it has follow up information till 8.832. Now suppose we want to look at the survival of ID 2 under joint model 1 after time 4 and for joint model 2 after time 9. For that we created the newdata as if the individual is followed till for a time period less than min(4,9).

Examples

 
 library(JMbayes2)
 library(FastJM)
  st_pbcid<-function(){
  new_pbcid<-pbc2.id
  new_pbcid$time_2<-rexp(n=nrow(pbc2.id),1/10)
  cen_time<-runif(nrow(pbc2.id),min(new_pbcid$time_2),max(new_pbcid$time_2))
  status_2<-ifelse(new_pbcid$time_2<cen_time,1,0)
  new_pbcid$status_2<-status_2
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<cen_time,new_pbcid$time_2,cen_time)
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<new_pbcid$years,new_pbcid$years,new_pbcid$time_2)
  new_pbcid}
new_pbc2id<-st_pbcid()
pbc2$status_2<-rep(new_pbc2id$status_2,times=data.frame(table(pbc2$id))$Freq)
pbc2$time_2<-rep(new_pbc2id$time_2,times=data.frame(table(pbc2$id))$Freq)
pbc2_new<-pbc2[pbc2$id%in%c(1:50),]
new_pbc2id<-new_pbc2id[new_pbc2id$id%in%c(1:50),]
model_jmcs<-jmcsB(dtlong=pbc2_new,dtsurv = new_pbc2id,
                  longm=list(serBilir~drug*year,
                             serBilir~drug*year),
                  survm=list(Surv(years,status2)~drug,
                             Surv(time_2,status_2)~drug+age),
                  rd=list(~1|id,~1|id),
                  id='id',timeVar='year')

t0<-4
nd<-pbc2[pbc2$id %in% c(2),]
nd<-nd[nd$year<t0,]
nd$status2<-0
nd$years<-t0
nd$time_2<-9
nd$status_2<-0
plot(x=model_jmcs,y=nd)
##
 

Prediction plot from jmrmlB()

Description

Prediction plot from jmrmlB()

Usage

## S3 method for class 'jmrmlB'
plot(x, y, ...)

Arguments

Value

Returns prediction plot for the newdata using the model fitted through jmrmlB().

Note

In the example code we use newdata as the data for ID 2 in the PBC2 dataset, it has follow up information till 8.832. Now suppose we want to look at the survival of ID 2 under joint model 1 after time 4 and for joint model 2 after time 9. For that we created the newdata as if the individual is followed till for a time period less than min(4,9).

Examples

 
##
library(JMbayes2)
library(joineRML)
jmrmlBModel<-jmrmlB(dtlong=new_long2[new_long2$id<=400,],
                    dtsurv=new_surv2[new_surv2$id<=400,],
                    longm=list(y~x7+visit,y~x7+visit),
                    survm=list(Surv(time,status)~x1+visit,
                               Surv(time_2,status_2)~x1+visit),
                    rd=list(~visit|id,~visit|id),
                    id='id',
                    timeVar='visit',
                    samplesize=200,
                    BIGdata=TRUE)
t0<-6
ndBIG<-new_long2[new_long2$id==10,]
ndBIG<-ndBIG[ndBIG$visit<t0,]
ndBIG$status<-0
ndBIG$time<-t0
ndBIG$time_2<-10
ndBIG$status_2<-0
plot(jmrmlBModel,ndBIG)
##

Prediction plot from jmstB()

Description

Prediction plot from jmstB()

Usage

## S3 method for class 'jmstB'
plot(x, y, ...)

Arguments

Value

Returns prediction plot for the newdata using the model fitted through jmstB().

Note

In the example code we use newdata as the data for ID 2 in the PBC2 dataset, it has follow up information till 8.832. Now suppose we want to look at the survival of ID 2 under joint model 1 after time 4 and for joint model 2 after time 9. For that we created the newdata as if the individual is followed till for a time period less than min(4,9).

Examples

 
##
library(JMbayes2)
library(rstanarm)
st_pbcid<-function(){
  new_pbcid<-pbc2.id
  new_pbcid$time_2<-rexp(n=nrow(pbc2.id),1/10)
  cen_time<-runif(nrow(pbc2.id),min(new_pbcid$time_2),max(new_pbcid$time_2))
  status_2<-ifelse(new_pbcid$time_2<cen_time,1,0)
  new_pbcid$status_2<-status_2
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<cen_time,new_pbcid$time_2,cen_time)
  new_pbcid$time_2<-ifelse(new_pbcid$time_2<new_pbcid$years,new_pbcid$years,new_pbcid$time_2)
  new_pbcid
}
new_pbc2id<-st_pbcid()
pbc2$status_2<-rep(new_pbc2id$status_2,times=data.frame(table(pbc2$id))$Freq)
pbc2$time_2<-rep(new_pbc2id$time_2,times=data.frame(table(pbc2$id))$Freq)
pbc2_new<-pbc2[pbc2$id%in%c(1:50),]
new_pbc2id<-new_pbc2id[new_pbc2id$id%in%c(1:50),]
model_jmstBdirect<-jmstB(
  dtlong=pbc2_new,
  dtsurv = new_pbc2id,
  longm=list(serBilir ~ drug * year+(year|id),albumin~drug+year+(year|id)),
  survm=list(Surv(years,status2) ~ drug,Surv(time_2,status_2) ~ drug),
  timeVar="year",
  id='id',
  refresh=400,
  nchain=1)
t0<-4
nd<-pbc2[pbc2$id %in% c(2), ]
nd<-nd[nd$year<t0,]
nd$status2<-0
nd$years<-t0
nd$time_2<-9
nd$status_2<-0
plot(x=model_jmstBdirect,y = nd)
##

predict.jmbB

Description

predict.jmbB

Usage

## S3 method for class 'jmbB'
predict(object, newdata, ...)

Arguments

Value

Survival Prediction for newdata from model fitted through jmbB()

predict.jmcsB

Description

predict.jmcsB

Usage

## S3 method for class 'jmcsB'
predict(object, newdata, ...)

Arguments

Value

Survival Prediction for newdata from model fitted through jmcsB()

predict.jmrmlB

Description

predict.jmrmlB

Usage

## S3 method for class 'jmrmlB'
predict(object, newdata, ...)

Arguments

Value

Survival Prediction for newdata from model fitted through jmrmlB()

predict.jmstB

Description

predict.jmstB

Usage

## S3 method for class 'jmstB'
predict(object, newdata, ...)

Arguments

Value

Survival Prediction for newdata from model fitted through jmstB()

print.

Description

print method for class 'jmbB'

Usage

## S3 method for class 'jmbB'
print(x, ...)

Arguments

Value

prints table containing various parameter estimates, SE, P- value for both survival and longitudinal submodel, if the model is bayesian it includes their credible interval too.

print.

Description

print method for class 'jmcsB'

Usage

## S3 method for class 'jmcsB'
print(x, ...)

Arguments

Value

prints table containing various parameter estimates, SE, P- value for both survival and longitudinal submodel, if the model is bayesian it includes their credible interval too.

print.

Description

print method for class 'jmrmlB'

Usage

## S3 method for class 'jmrmlB'
print(x, ...)

Arguments

Value

prints table containing various parameter estimates, SE, P- value for both survival and longitudinal submodel, if the model is bayesian it includes their credible interval too.

print.

Description

print method for class 'jmstB'

Usage

## S3 method for class 'jmstB'
print(x, ...)

Arguments

Value

prints table containing various parameter estimates, SE, P- value for both survival and longitudinal submodel, if the model is bayesian it includes their credible interval too.