Description
Fits a shared parameter joint model for longitudinal and time-to-event
(e.g. survival) data under a Bayesian framework using Stan.Usage
Arguments
Value
Details
See Also
Examples
Run this code# NOT RUN {
if (.Platform$OS.type != "windows" || .Platform$r_arch !="i386") {
#####
# Univariate joint model, with association structure based on the
# current value of the linear predictor
f1 <- stan_jm(formulaLong = logBili ~ year + (1 | id),
dataLong = pbcLong,
formulaEvent = Surv(futimeYears, death) ~ sex + trt,
dataEvent = pbcSurv,
time_var = "year",
# this next line is only to keep the example small in size!
chains = 1, cores = 1, seed = 12345, iter = 1000)
print(f1)
summary(f1)
#####
# Univariate joint model, with association structure based on the
# current value and slope of the linear predictor
f2 <- stan_jm(formulaLong = logBili ~ year + (year | id),
dataLong = pbcLong,
formulaEvent = Surv(futimeYears, death) ~ sex + trt,
dataEvent = pbcSurv,
assoc = c("etavalue", "etaslope"),
time_var = "year",
chains = 1, cores = 1, seed = 12345, iter = 1000)
print(f2)
#####
# Univariate joint model, with association structure based on the
# lagged value of the linear predictor, where the lag is 2 time
# units (i.e. 2 years in this example)
f3 <- stan_jm(formulaLong = logBili ~ year + (1 | id),
dataLong = pbcLong,
formulaEvent = Surv(futimeYears, death) ~ sex + trt,
dataEvent = pbcSurv,
time_var = "year",
assoc = "etavalue", lag_assoc = 2,
chains = 1, cores = 1, seed = 12345, iter = 1000)
print(f3)
#####
# Univariate joint model, where the association structure includes
# interactions with observed data. Here we specify that we want to use
# an association structure based on the current value of the linear
# predictor from the longitudinal submodel (i.e. "etavalue"), but we
# also want to interact this with the treatment covariate (trt) from
# pbcLong data frame, so that we can estimate a different association
# parameter (i.e. estimated effect of log serum bilirubin on the log
# hazard of death) for each treatment group
f4 <- stan_jm(formulaLong = logBili ~ year + (1 | id),
dataLong = pbcLong,
formulaEvent = Surv(futimeYears, death) ~ sex + trt,
dataEvent = pbcSurv,
time_var = "year",
assoc = c("etavalue", "etavalue_data(~ trt)"),
chains = 1, cores = 1, seed = 12345, iter = 1000)
print(f4)
######
# Multivariate joint model, with association structure based
# on the current value and slope of the linear predictor in the
# first longitudinal submodel and the area under the marker
# trajectory for the second longitudinal submodel
mv1 <- stan_jm(
formulaLong = list(
logBili ~ year + (1 | id),
albumin ~ sex + year + (year | id)),
dataLong = pbcLong,
formulaEvent = Surv(futimeYears, death) ~ sex + trt,
dataEvent = pbcSurv,
assoc = list(c("etavalue", "etaslope"), "etaauc"),
time_var = "year",
chains = 1, cores = 1, seed = 12345, iter = 100)
print(mv1)
#####
# Multivariate joint model, where the association structure is formed by
# including the expected value of each longitudinal marker (logBili and
# albumin) in the linear predictor of the event submodel, as well as their
# interaction effect (i.e. the interaction between the two "etavalue" terms).
# Note that whether such an association structure based on a marker by
# marker interaction term makes sense will depend on the context of your
# application -- here we just show it for demostration purposes).
mv2 <- stan_jm(
formulaLong = list(
logBili ~ year + (1 | id),
albumin ~ sex + year + (year | id)),
dataLong = pbcLong,
formulaEvent = Surv(futimeYears, death) ~ sex + trt,
dataEvent = pbcSurv,
assoc = list(c("etavalue", "etavalue_etavalue(2)"), "etavalue"),
time_var = "year",
chains = 1, cores = 1, seed = 12345, iter = 100)
#####
# Multivariate joint model, with one bernoulli marker and one
# Gaussian marker. We will artificially create the bernoulli
# marker by dichotomising log serum bilirubin
pbcLong$ybern <- as.integer(pbcLong$logBili >= mean(pbcLong$logBili))
mv3 <- stan_jm(
formulaLong = list(
ybern ~ year + (1 | id),
albumin ~ sex + year + (year | id)),
dataLong = pbcLong,
formulaEvent = Surv(futimeYears, death) ~ sex + trt,
dataEvent = pbcSurv,
family = list(binomial, gaussian),
time_var = "year",
chains = 1, cores = 1, seed = 12345, iter = 1000)
}
# }
# NOT RUN {
# }
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