jm.surv: Dynamic predictions of survival probabilites
In sa4khan/AFTjmr: Recurrent event analysis and joint modeling using accelerated failure time (AFT) models

Description Usage Arguments Details Value Author(s) References See Also Examples

Dynamic predictions of survival probabilites P(T >= t | T >= s) for t >= s, given longitudinal measurements, covariate information, and survival up to time s.

1 2	jm.surv(bayes.fit, newsurvdata, newlongdata, st = NULL, control = list())

`bayes.fit`	fit of the joint model using `jmreg.aft`.
`newsurvdata`	a data frame that contains covariate information for the time-to-event process. Names of the variables in `newsurvdata` must be the same as in the data frame that was used to fit the survival model using `coxph`.
`newlongdata`	a data frame that contains repeated measurements and covariate information for the longitudinal process. Names of the variables in `newlongdata` must be the same as in the data frame that was used to fit the linear mixed effects model using `lme`.
`st`	a numeric vector of times at which survival probabilities `P(T >= t \| T >= s)` are to be computed. The first element must be `s`. If `NULL`, `s` is taken to be the last time at which the subject provided a longitudinal measurement, and `t` is `seq(s,max.st,length=15)`, where `max.st` is `floor(max(event times))`.
`control`	a list of control values (see Details).

For estimation, we consider a Monte Carlo approach similar to the algorithm proposed by Rizopoulos (2016) to construct a subject's profile (i.e., drawing a sample from the posterior distribution of b_i), given a realization of the population parameters from the MCMC samples of the fitted joint model and survival up to time s. This is implemented in JAGS (Plummer, 2017).

control: a list of control values with components:

n.sample: size of the MCMC sample to be used to derive the posterior summeries of the conditional probabilities (default is 200).
n.adapt: number of steps for adaptation to draw b_i (random effects), given a realization of the population parameters from the MCMC samples of the fitted joint model and urvival up to time s (default is 1000).
n.update: burn-in iterations to draw b_i (random effects), given a realization of the population parameters from the MCMC samples of the fitted joint model and urvival up to time s (default is 5000).

Posterior summeries of the survival probabilities.

Shahedul Khan <khan@math.usask.ca>

Plummer M, JAGS Version 4.3.0 user manual, 2017, http://people.stat.sc.edu/hansont/stat740/jags_user_manual.pdf.

Rizopoulos D, The R package JMbayes for fitting joint models for longitudinal and time-to-event data Using MCMC, Journal of Statistical Software, 72(7): 1-45, 2016.

jmreg.aft, jm.summary

  library(JM)
  data(aids.id)
  data(aids)
  surv.fit<-coxph(Surv(Time,death)~drug+gender+prevOI+AZT,
          data=aids.id,x=TRUE)
  lme.fit<-lme(CD4~obstime+obstime:drug+gender+prevOI+AZT,
         random=~obstime|patient,data=aids)
  jmfit.gg<-jmreg.aft(surv.fit=surv.fit,lme.fit=lme.fit,
         surv.model="ggamma",rand.model="simple",timevar="obstime",
         n.chain=2,n.adapt = 5000, n.burn = 15000, 
         n.iter = 150000, n.thin = 5)
  newsurvdata<-data.frame(aids.id[aids.id$patient==409,])
  newlongdata<-data.frame(aids[aids$patient==409,])
  surv.id0<-jm.surv(bayes.fit=jmfit.gg,newsurvdata=newsurvdata,newlongdata=newlongdata)
  newsurvdata<-data.frame(aids.id[aids.id$patient==404,])
  newlongdata<-data.frame(aids[aids$patient==404,])
  surv.id1<-jm.surv(bayes.fit=jmfit.gg,newsurvdata=newsurvdata,newlongdata=newlongdata)
  plot(surv.id1[[1]][,1],surv.id1[[1]][,2],ylim=c(0.38,1),
       type="l",lty=2,xlab="Time (t)", ylab="P(T > t | T > 12)")
  lines(surv.id0[[1]][,1],surv.id0[[1]][,2],lty=1)
  legend("topright",legend=c("Patient id # 409","Patient id # 404"),lty=c(1,2))