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Bayesian joint longitudinal and time-to-event models via Stan

Source: R/stan_jm.R
stan_jm.Rd

Fits a shared parameter joint model for longitudinal and time-to-event (e.g. survival) data under a Bayesian framework using Stan.

Usage

stan_jm(
  formulaLong,
  dataLong,
  formulaEvent,
  dataEvent,
  time_var,
  id_var,
  family = gaussian,
  assoc = "etavalue",
  lag_assoc = 0,
  grp_assoc,
  scale_assoc = NULL,
  epsilon = 1e-05,
  basehaz = c("bs", "weibull", "piecewise"),
  basehaz_ops,
  qnodes = 15,
  init = "prefit",
  weights,
  priorLong = normal(autoscale = TRUE),
  priorLong_intercept = normal(autoscale = TRUE),
  priorLong_aux = cauchy(0, 5, autoscale = TRUE),
  priorEvent = normal(autoscale = TRUE),
  priorEvent_intercept = normal(autoscale = TRUE),
  priorEvent_aux = cauchy(autoscale = TRUE),
  priorEvent_assoc = normal(autoscale = TRUE),
  prior_covariance = lkj(autoscale = TRUE),
  prior_PD = FALSE,
  algorithm = c("sampling", "meanfield", "fullrank"),
  adapt_delta = NULL,
  max_treedepth = 10L,
  QR = FALSE,
  sparse = FALSE,
  ...
)

Arguments

formulaLong

A two-sided linear formula object describing both the fixed-effects and random-effects parts of the longitudinal submodel, similar in vein to formula specification in the lme4 package (see glmer or the lme4 vignette for details). Note however that the double bar (||) notation is not allowed when specifying the random-effects parts of the formula, and neither are nested grouping factors (e.g. (1 | g1/g2)) or (1 | g1:g2), where g1, g2 are grouping factors. Offset terms can also be included in the model formula. For a multivariate joint model (i.e. more than one longitudinal marker) this should be a list of such formula objects, with each element of the list providing the formula for one of the longitudinal submodels.

dataLong

A data frame containing the variables specified in formulaLong. If fitting a multivariate joint model, then this can be either a single data frame which contains the data for all longitudinal submodels, or it can be a list of data frames where each element of the list provides the data for one of the longitudinal submodels.

formulaEvent

A two-sided formula object describing the event submodel. The left hand side of the formula should be a Surv() object. See Surv.

dataEvent

A data frame containing the variables specified in formulaEvent.

time_var

A character string specifying the name of the variable in dataLong which represents time.

id_var

A character string specifying the name of the variable in dataLong which distinguishes between individuals. This can be left unspecified if there is only one grouping factor (which is assumed to be the individual). If there is more than one grouping factor (i.e. clustering beyond the level of the individual) then the id_var argument must be specified.

family

The family (and possibly also the link function) for the longitudinal submodel(s). See glmer for details. If fitting a multivariate joint model, then this can optionally be a list of families, in which case each element of the list specifies the family for one of the longitudinal submodels.

assoc

A character string or character vector specifying the joint model association structure. Possible association structures that can be used include: "etavalue" (the default); "etaslope"; "etaauc"; "muvalue"; "muslope"; "muauc"; "shared_b"; "shared_coef"; or "null". These are described in the Details section below. For a multivariate joint model, different association structures can optionally be used for each longitudinal submodel by specifying a list of character vectors, with each element of the list specifying the desired association structure for one of the longitudinal submodels. Specifying assoc = NULL will fit a joint model with no association structure (equivalent to fitting separate longitudinal and time-to-event models). It is also possible to include interaction terms between the association term ("etavalue", "etaslope", "muvalue", "muslope") and observed data/covariates. It is also possible, when fitting a multivariate joint model, to include interaction terms between the association terms ("etavalue" or "muvalue") corresponding to the different longitudinal outcomes. See the Details section as well as the Examples below.

lag_assoc

A non-negative scalar specifying the time lag that should be used for the association structure. That is, the hazard of the event at time t will be assumed to be associated with the value/slope/auc of the longitudinal marker at time t-u, where u is the time lag. If fitting a multivariate joint model, then a different time lag can be used for each longitudinal marker by providing a numeric vector of lags, otherwise if a scalar is provided then the specified time lag will be used for all longitudinal markers. Note however that only one time lag can be specified for linking each longitudinal marker to the event, and that that time lag will be used for all association structure types (e.g. "etavalue", "etaslope", "etaauc", "muvalue", etc) that are specified for that longitudinal marker in the assoc argument.

grp_assoc

Character string specifying the method for combining information across lower level units clustered within an individual when forming the association structure. This is only relevant when a grouping factor is specified in formulaLong that corresponds to clustering within individuals. This can be specified as either "sum", mean, "min" or "max". For example, specifying grp_assoc = "sum" indicates that the association structure should be based on a summation across the lower level units clustered within an individual, or specifying grp_assoc = "mean" indicates that the association structure should be based on the mean (i.e. average) taken across the lower level units clustered within an individual. So, for example, specifying assoc = "muvalue" and grp_assoc = "sum" would mean that the log hazard at time t for individual i would be linearly related to the sum of the expected values at time t for each of the lower level units (which may be for example tumor lesions) clustered within that individual.

scale_assoc

A non-zero numeric value specifying an optional scaling parameter for the association structure. This multiplicatively scales the value/slope/auc of the longitudinal marker by scale_assoc within the event submodel. When fitting a multivariate joint model, a scaling parameter must be specified for each longitudinal submodel using a vector of numeric values. Note that only one scaling parameter can be specified for each longitudinal submodel, and it will be used for all association structure types (e.g. "etavalue", "etaslope", "etaauc", "muvalue", etc) that are specified for that longitudinal marker in the assoc argument.

epsilon

The half-width of the central difference used to numerically calculate the derivate when the "etaslope" association structure is used.

basehaz

A character string indicating which baseline hazard to use for the event submodel. Options are a B-splines approximation estimated for the log baseline hazard ("bs", the default), a Weibull baseline hazard ("weibull"), or a piecewise constant baseline hazard ("piecewise"). (Note however that there is currently limited post-estimation functionality available for models estimated using a piecewise constant baseline hazard).

basehaz_ops

A named list specifying options related to the baseline hazard. Currently this can include:

df

A positive integer specifying the degrees of freedom for the B-splines if basehaz = "bs", or the number of intervals used for the piecewise constant baseline hazard if basehaz = "piecewise". The default is 6.

knots

An optional numeric vector specifying the internal knot locations for the B-splines if basehaz = "bs", or the internal cut-points for defining intervals of the piecewise constant baseline hazard if basehaz = "piecewise". Knots cannot be specified if df is specified. If not specified, then the default is to use df - 4 knots if basehaz = "bs", or df - 1 knots if basehaz = "piecewise", which are placed at equally spaced percentiles of the distribution of observed event times.

qnodes

The number of nodes to use for the Gauss-Kronrod quadrature that is used to evaluate the cumulative hazard in the likelihood function. Options are 15 (the default), 11 or 7.

init

The method for generating the initial values for the MCMC. The default is "prefit", which uses those obtained from fitting separate longitudinal and time-to-event models prior to fitting the joint model. The separate longitudinal model is a (possibly multivariate) generalised linear mixed model estimated using variational bayes. This is achieved via the stan_mvmer function with algorithm = "meanfield". The separate Cox model is estimated using coxph. This is achieved using the and time-to-event models prior to fitting the joint model. The separate models are estimated using the glmer and coxph functions. This should provide reasonable initial values which should aid the MCMC sampler. Parameters that cannot be obtained from fitting separate longitudinal and time-to-event models are initialised using the "random" method for stan. However it is recommended that any final analysis should ideally be performed with several MCMC chains each initiated from a different set of initial values; this can be obtained by setting init = "random". In addition, other possibilities for specifying init are the same as those described for stan.

weights

Experimental and should be used with caution. The user can optionally supply a 2-column data frame containing a set of 'prior weights' to be used in the estimation process. The data frame should contain two columns: the first containing the IDs for each individual, and the second containing the corresponding weights. The data frame should only have one row for each individual; that is, weights should be constant within individuals.

priorLong, priorEvent, priorEvent_assoc

The prior distributions for the regression coefficients in the longitudinal submodel(s), event submodel, and the association parameter(s). Can be a call to one of the various functions provided by rstanarm for specifying priors. The subset of these functions that can be used for the prior on the coefficients can be grouped into several "families":

FamilyFunctions
Student t familynormal, student_t, cauchy
Hierarchical shrinkage familyhs, hs_plus
Laplace familylaplace, lasso

See the priors help page for details on the families and how to specify the arguments for all of the functions in the table above. To omit a prior —i.e., to use a flat (improper) uniform prior— prior can be set to NULL, although this is rarely a good idea.

Note: Unless QR=TRUE, if prior is from the Student t family or Laplace family, and if the autoscale argument to the function used to specify the prior (e.g. normal) is left at its default and recommended value of TRUE, then the default or user-specified prior scale(s) may be adjusted internally based on the scales of the predictors. See the priors help page for details on the rescaling and the prior_summary function for a summary of the priors used for a particular model.

priorLong_intercept, priorEvent_intercept

The prior distributions for the intercepts in the longitudinal submodel(s) and event submodel. Can be a call to normal, student_t or cauchy. See the priors help page for details on these functions. To omit a prior on the intercept —i.e., to use a flat (improper) uniform prior— prior_intercept can be set to NULL.

Note: The prior distribution for the intercept is set so it applies to the value when all predictors are centered. Moreover, note that a prior is only placed on the intercept for the event submodel when a Weibull baseline hazard has been specified. For the B-splines and piecewise constant baseline hazards there is not intercept parameter that is given a prior distribution; an intercept parameter will be shown in the output for the fitted model, but this just corresponds to the necessary post-estimation adjustment in the linear predictor due to the centering of the predictiors in the event submodel.

priorLong_aux

The prior distribution for the "auxiliary" parameters in the longitudinal submodels (if applicable). The "auxiliary" parameter refers to a different parameter depending on the family. For Gaussian models priorLong_aux controls "sigma", the error standard deviation. For negative binomial models priorLong_aux controls "reciprocal_dispersion", which is similar to the "size" parameter of rnbinom: smaller values of "reciprocal_dispersion" correspond to greater dispersion. For gamma models priorLong_aux sets the prior on to the "shape" parameter (see e.g., rgamma), and for inverse-Gaussian models it is the so-called "lambda" parameter (which is essentially the reciprocal of a scale parameter). Binomial and Poisson models do not have auxiliary parameters.

priorLong_aux can be a call to exponential to use an exponential distribution, or normal, student_t or cauchy, which results in a half-normal, half-t, or half-Cauchy prior. See priors for details on these functions. To omit a prior —i.e., to use a flat (improper) uniform prior— set priorLong_aux to NULL.

If fitting a multivariate joint model, you have the option to specify a list of prior distributions, however the elements of the list that correspond to any longitudinal submodel which does not have an auxiliary parameter will be ignored.

priorEvent_aux

The prior distribution for the "auxiliary" parameters in the event submodel. The "auxiliary" parameters refers to different parameters depending on the baseline hazard. For basehaz = "weibull" the auxiliary parameter is the Weibull shape parameter. For basehaz = "bs" the auxiliary parameters are the coefficients for the B-spline approximation to the log baseline hazard. For basehaz = "piecewise" the auxiliary parameters are the piecewise estimates of the log baseline hazard.

prior_covariance

Cannot be NULL; see priors for more information about the prior distributions on covariance matrices. Note however that the default prior for covariance matrices in stan_jm is slightly different to that in stan_glmer (the details of which are described on the priors page).

prior_PD

A logical scalar (defaulting to FALSE) indicating whether to draw from the prior predictive distribution instead of conditioning on the outcome.

algorithm

A string (possibly abbreviated) indicating the estimation approach to use. Can be "sampling" for MCMC (the default), "optimizing" for optimization, "meanfield" for variational inference with independent normal distributions, or "fullrank" for variational inference with a multivariate normal distribution. See rstanarm-package for more details on the estimation algorithms. NOTE: not all fitting functions support all four algorithms.

adapt_delta

Only relevant if algorithm="sampling". See the adapt_delta help page for details.

max_treedepth

A positive integer specifying the maximum treedepth for the non-U-turn sampler. See the control argument in stan.

QR

A logical scalar defaulting to FALSE, but if TRUE applies a scaled qr decomposition to the design matrix. The transformation does not change the likelihood of the data but is recommended for computational reasons when there are multiple predictors. See the QR-argument documentation page for details on how rstanarm does the transformation and important information about how to interpret the prior distributions of the model parameters when using QR=TRUE.

sparse

A logical scalar (defaulting to FALSE) indicating whether to use a sparse representation of the design (X) matrix. If TRUE, the the design matrix is not centered (since that would destroy the sparsity) and likewise it is not possible to specify both QR = TRUE and sparse = TRUE. Depending on how many zeros there are in the design matrix, setting sparse = TRUE may make the code run faster and can consume much less RAM.

...

Further arguments passed to the function in the rstan package (sampling, vb, or optimizing), corresponding to the estimation method named by algorithm. For example, if algorithm is "sampling" it is possible to specify iter, chains, cores, and other MCMC controls.

Another useful argument that can be passed to rstan via ... is refresh, which specifies how often to print updates when sampling (i.e., show the progress every refresh iterations). refresh=0 turns off the iteration updates.

Value

A stanjm object is returned.

Details

The stan_jm function can be used to fit a joint model (also known as a shared parameter model) for longitudinal and time-to-event data under a Bayesian framework. The underlying estimation is carried out using the Bayesian C++ package Stan (https://mc-stan.org/).

The joint model may be univariate (with only one longitudinal submodel) or multivariate (with more than one longitudinal submodel). For the longitudinal submodel a (possibly multivariate) generalised linear mixed model is assumed with any of the family choices allowed by glmer. If a multivariate joint model is specified (by providing a list of formulas in the formulaLong argument), then the multivariate longitudinal submodel consists of a multivariate generalized linear model (GLM) with group-specific terms that are assumed to be correlated across the different GLM submodels. That is, within a grouping factor (for example, patient ID) the group-specific terms are assumed to be correlated across the different GLM submodels. It is possible to specify a different outcome type (for example a different family and/or link function) for each of the GLM submodels, by providing a list of family objects in the family argument. Multi-level clustered data are allowed, and that additional clustering can occur at a level higher than the individual-level (e.g. patients clustered within clinics), or at a level lower than the individual-level (e.g. tumor lesions clustered within patients). If the clustering occurs at a level lower than the individual, then the user needs to indicate how the lower level clusters should be handled when forming the association structure between the longitudinal and event submodels (see the grp_assoc argument described above).

For the event submodel a parametric proportional hazards model is assumed. The baseline hazard can be estimated using either a cubic B-splines approximation (basehaz = "bs", the default), a Weibull distribution (basehaz = "weibull"), or a piecewise constant baseline hazard (basehaz = "piecewise"). If the B-spline or piecewise constant baseline hazards are used, then the degrees of freedom or the internal knot locations can be (optionally) specified. If the degrees of freedom are specified (through the df argument) then the knot locations are automatically generated based on the distribution of the observed event times (not including censoring times). Otherwise internal knot locations can be specified directly through the knots argument. If neither df or knots is specified, then the default is to set df equal to 6. It is not possible to specify both df and knots.

Time-varying covariates are allowed in both the longitudinal and event submodels. These should be specified in the data in the same way as they normally would when fitting a separate longitudinal model using lmer or a separate time-to-event model using coxph. These time-varying covariates should be exogenous in nature, otherwise they would perhaps be better specified as an additional outcome (i.e. by including them as an additional longitudinal outcome in the joint model).

Bayesian estimation of the joint model is performed via MCMC. The Bayesian model includes independent priors on the regression coefficients for both the longitudinal and event submodels, including the association parameter(s) (in much the same way as the regression parameters in stan_glm) and priors on the terms of a decomposition of the covariance matrices of the group-specific parameters. See priors for more information about the priors distributions that are available.

Gauss-Kronrod quadrature is used to numerically evaluate the integral over the cumulative hazard in the likelihood function for the event submodel. The accuracy of the numerical approximation can be controlled using the number of quadrature nodes, specified through the qnodes argument. Using a higher number of quadrature nodes will result in a more accurate approximation.

Association structures

The association structure for the joint model can be based on any of the following parameterisations:

  • current value of the linear predictor in the longitudinal submodel ("etavalue")

  • first derivative (slope) of the linear predictor in the longitudinal submodel ("etaslope")

  • the area under the curve of the linear predictor in the longitudinal submodel ("etaauc")

  • current expected value of the longitudinal submodel ("muvalue")

  • the area under the curve of the expected value from the longitudinal submodel ("muauc")

  • shared individual-level random effects ("shared_b")

  • shared individual-level random effects which also incorporate the corresponding fixed effect as well as any corresponding random effects for clustering levels higher than the individual) ("shared_coef")

  • interactions between association terms and observed data/covariates ("etavalue_data", "etaslope_data", "muvalue_data", "muslope_data"). These are described further below.

  • interactions between association terms corresponding to different longitudinal outcomes in a multivariate joint model ("etavalue_etavalue(#)", "etavalue_muvalue(#)", "muvalue_etavalue(#)", "muvalue_muvalue(#)"). These are described further below.

  • no association structure (equivalent to fitting separate longitudinal and event models) ("null" or NULL)

More than one association structure can be specified, however, not all possible combinations are allowed. Note that for the lagged association structures baseline values (time = 0) are used for the instances where the time lag results in a time prior to baseline. When using the "etaauc" or "muauc" association structures, the area under the curve is evaluated using Gauss-Kronrod quadrature with 15 quadrature nodes. By default, "shared_b" and "shared_coef" contribute all random effects to the association structure; however, a subset of the random effects can be chosen by specifying their indices between parentheses as a suffix, for example, "shared_b(1)" or "shared_b(1:3)" or "shared_b(1,2,4)", and so on.

In addition, several association terms ("etavalue", "etaslope", "muvalue", "muslope") can be interacted with observed data/covariates. To do this, use the association term's main handle plus a suffix of "_data" then followed by the model matrix formula in parentheses. For example if we had a variable in our dataset for gender named sex then we might want to obtain different estimates for the association between the current slope of the marker and the risk of the event for each gender. To do this we would specify assoc = c("etaslope", "etaslope_data(~ sex)").

It is also possible, when fitting a multivariate joint model, to include interaction terms between the association terms themselves (this only applies for interacting "etavalue" or "muvalue"). For example, if we had a joint model with two longitudinal markers, we could specify assoc = list(c("etavalue", "etavalue_etavalue(2)"), "etavalue"). The first element of list says we want to use the value of the linear predictor for the first marker, as well as it's interaction with the value of the linear predictor for the second marker. The second element of the list says we want to also include the expected value of the second marker (i.e. as a "main effect"). Therefore, the linear predictor for the event submodel would include the "main effects" for each marker as well as their interaction.

There are additional examples in the Examples section below.

See also

stanreg-objects, stanmvreg-methods, print.stanmvreg, summary.stanmvreg, posterior_traj, posterior_survfit, posterior_predict, posterior_interval, pp_check, ps_check, stan_mvmer.

Examples

if (.Platform$OS.type != "windows" || .Platform$r_arch !="i386") {
# \donttest{

#####
# 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)
# }
}
#> Fitting a univariate joint model.
#> 
#> Please note the warmup may be much slower than later iterations!
#> 
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000174 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.74 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
#> Chain 1: Iteration: 100 / 1000 [ 10%]  (Warmup)
#> Chain 1: Iteration: 200 / 1000 [ 20%]  (Warmup)
#> Chain 1: Iteration: 300 / 1000 [ 30%]  (Warmup)
#> Chain 1: Iteration: 400 / 1000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 500 / 1000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 501 / 1000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 600 / 1000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 700 / 1000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 800 / 1000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 900 / 1000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 3.251 seconds (Warm-up)
#> Chain 1:                2.201 seconds (Sampling)
#> Chain 1:                5.452 seconds (Total)
#> Chain 1: 
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> stan_jm
#>  formula (Long1): logBili ~ year + (1 | id)
#>  family  (Long1): gaussian [identity]
#>  formula (Event): Surv(futimeYears, death) ~ sex + trt
#>  baseline hazard: bs
#>  assoc:           etavalue (Long1)
#> ------
#> 
#> Longitudinal submodel: logBili
#>             Median MAD_SD
#> (Intercept) 0.802  0.203 
#> year        0.092  0.010 
#> sigma       0.509  0.026 
#> 
#> Event submodel:
#>                 Median MAD_SD exp(Median)
#> (Intercept)     -3.068  0.569  0.046     
#> sexf            -0.376  0.538  0.687     
#> trt             -0.719  0.506  0.487     
#> Long1|etavalue   1.449  0.281  4.257     
#> b-splines-coef1 -1.418  1.106     NA     
#> b-splines-coef2  0.222  0.859     NA     
#> b-splines-coef3 -1.768  1.192     NA     
#> b-splines-coef4  1.058  1.450     NA     
#> b-splines-coef5 -0.460  1.602     NA     
#> b-splines-coef6 -0.369  1.558     NA     
#> 
#> Group-level error terms:
#>  Groups Name              Std.Dev.
#>  id     Long1|(Intercept) 1.323   
#> Num. levels: id 40 
#> 
#> Sample avg. posterior predictive distribution 
#> of longitudinal outcomes:
#>                Median MAD_SD
#> Long1|mean_PPD 0.586  0.043 
#> 
#> ------
#> For info on the priors used see help('prior_summary.stanreg').Fitting a univariate joint model.
#> 
#> Please note the warmup may be much slower than later iterations!
#> 
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000317 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 21.503 seconds (Warm-up)
#> Chain 1:                15.066 seconds (Sampling)
#> Chain 1:                36.569 seconds (Total)
#> Chain 1: 
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> stan_jm
#>  formula (Long1): logBili ~ year + (year | id)
#>  family  (Long1): gaussian [identity]
#>  formula (Event): Surv(futimeYears, death) ~ sex + trt
#>  baseline hazard: bs
#>  assoc:           etavalue (Long1), etaslope (Long1)
#> ------
#> 
#> Longitudinal submodel: logBili
#>             Median MAD_SD
#> (Intercept) 0.625  0.229 
#> year        0.249  0.063 
#> sigma       0.357  0.017 
#> 
#> Event submodel:
#>                 Median    MAD_SD    exp(Median)
#> (Intercept)        -3.416     0.757     0.033  
#> sexf               -0.440     0.720     0.644  
#> trt                -0.987     0.640     0.373  
#> Long1|etavalue      0.821     0.427     2.273  
#> Long1|etaslope      9.538     5.737 13874.955  
#> b-splines-coef1    -5.377     4.425        NA  
#> b-splines-coef2    -2.087     2.338        NA  
#> b-splines-coef3    -2.848     1.555        NA  
#> b-splines-coef4    -0.511     1.833        NA  
#> b-splines-coef5     0.326     1.747        NA  
#> b-splines-coef6    -0.537     1.694        NA  
#> 
#> Group-level error terms:
#>  Groups Name              Std.Dev. Corr
#>  id     Long1|(Intercept) 1.284        
#>         Long1|year        0.241    0.67
#> Num. levels: id 40 
#> 
#> Sample avg. posterior predictive distribution 
#> of longitudinal outcomes:
#>                Median MAD_SD
#> Long1|mean_PPD 0.585  0.027 
#> 
#> ------
#> For info on the priors used see help('prior_summary.stanreg').Fitting a univariate joint model.
#> 
#> Please note the warmup may be much slower than later iterations!
#> 
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000161 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.61 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 2.727 seconds (Warm-up)
#> Chain 1:                2.179 seconds (Sampling)
#> Chain 1:                4.906 seconds (Total)
#> Chain 1: 
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> stan_jm
#>  formula (Long1): logBili ~ year + (1 | id)
#>  family  (Long1): gaussian [identity]
#>  formula (Event): Surv(futimeYears, death) ~ sex + trt
#>  baseline hazard: bs
#>  assoc:           etavalue (Long1)
#> ------
#> 
#> Longitudinal submodel: logBili
#>             Median MAD_SD
#> (Intercept) 0.838  0.188 
#> year        0.092  0.010 
#> sigma       0.509  0.023 
#> 
#> Event submodel:
#>                 Median MAD_SD exp(Median)
#> (Intercept)     -2.910  0.631  0.054     
#> sexf            -0.380  0.591  0.684     
#> trt             -0.703  0.489  0.495     
#> Long1|etavalue   1.454  0.254  4.281     
#> b-splines-coef1 -1.600  1.023     NA     
#> b-splines-coef2  0.250  0.898     NA     
#> b-splines-coef3 -1.518  1.312     NA     
#> b-splines-coef4  0.913  1.666     NA     
#> b-splines-coef5 -0.029  1.747     NA     
#> b-splines-coef6 -0.174  1.602     NA     
#> 
#> Group-level error terms:
#>  Groups Name              Std.Dev.
#>  id     Long1|(Intercept) 1.325   
#> Num. levels: id 40 
#> 
#> Sample avg. posterior predictive distribution 
#> of longitudinal outcomes:
#>                Median MAD_SD
#> Long1|mean_PPD 0.585  0.042 
#> 
#> ------
#> For info on the priors used see help('prior_summary.stanreg').Fitting a univariate joint model.
#> 
#> Please note the warmup may be much slower than later iterations!
#> 
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.00018 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.8 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 4.006 seconds (Warm-up)
#> Chain 1:                2.821 seconds (Sampling)
#> Chain 1:                6.827 seconds (Total)
#> Chain 1: 
#> Warning: The largest R-hat is 1.06, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> stan_jm
#>  formula (Long1): logBili ~ year + (1 | id)
#>  family  (Long1): gaussian [identity]
#>  formula (Event): Surv(futimeYears, death) ~ sex + trt
#>  baseline hazard: bs
#>  assoc:           etavalue (Long1), etavalue_data (Long1)
#> ------
#> 
#> Longitudinal submodel: logBili
#>             Median MAD_SD
#> (Intercept) 0.830  0.206 
#> year        0.092  0.008 
#> sigma       0.508  0.022 
#> 
#> Event submodel:
#>                    Median MAD_SD exp(Median)
#> (Intercept)        -3.140  0.690  0.043     
#> sexf               -0.366  0.629  0.694     
#> trt                -0.184  0.929  0.832     
#> Long1|etavalue      1.598  0.323  4.945     
#> Long1|etavalue:trt -0.374  0.574  0.688     
#> b-splines-coef1    -1.477  1.254     NA     
#> b-splines-coef2     0.038  0.856     NA     
#> b-splines-coef3    -1.639  1.234     NA     
#> b-splines-coef4     0.736  1.674     NA     
#> b-splines-coef5    -0.292  1.705     NA     
#> b-splines-coef6    -0.227  1.665     NA     
#> 
#> Group-level error terms:
#>  Groups Name              Std.Dev.
#>  id     Long1|(Intercept) 1.345   
#> Num. levels: id 40 
#> 
#> Sample avg. posterior predictive distribution 
#> of longitudinal outcomes:
#>                Median MAD_SD
#> Long1|mean_PPD 0.588  0.039 
#> 
#> ------
#> For info on the priors used see help('prior_summary.stanreg').Fitting a multivariate joint model.
#> 
#> Please note the warmup may be much slower than later iterations!
#> 
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1: Rejecting initial value:
#> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1:   Stan can't start sampling from this initial value.
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.001634 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.34 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1:          three stages of adaptation as currently configured.
#> Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1:          the given number of warmup iterations:
#> Chain 1:            init_buffer = 7
#> Chain 1:            adapt_window = 38
#> Chain 1:            term_buffer = 5
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 23.137 seconds (Warm-up)
#> Chain 1:                0.632 seconds (Sampling)
#> Chain 1:                23.769 seconds (Total)
#> Chain 1: 
#> Warning: There were 50 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 2.15, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Warning: Markov chains did not converge! Do not analyze results!
#> stan_jm
#>  formula (Long1): logBili ~ year + (1 | id)
#>  family  (Long1): gaussian [identity]
#>  formula (Long2): albumin ~ sex + year + (year | id)
#>  family  (Long2): gaussian [identity]
#>  formula (Event): Surv(futimeYears, death) ~ sex + trt
#>  baseline hazard: bs
#>  assoc:           etavalue (Long1), etaslope (Long1), etaauc (Long2)
#> ------
#> 
#> Longitudinal submodel 1: logBili
#>             Median MAD_SD
#> (Intercept) 0.697  0.000 
#> year        0.086  0.000 
#> sigma       0.526  0.000 
#> 
#> Longitudinal submodel 2: albumin
#>             Median MAD_SD
#> (Intercept)  3.509  0.000
#> sexf         0.081  0.000
#> year        -0.123  0.000
#> sigma        0.334  0.000
#> 
#> Event submodel:
#>                 Median        MAD_SD        exp(Median)  
#> (Intercept)      1.082885e+11  1.330000e-01           Inf
#> sexf            -3.470000e-01  0.000000e+00  7.070000e-01
#> trt             -9.600000e-02  0.000000e+00  9.080000e-01
#> Long1|etavalue   3.422000e+00  0.000000e+00  3.064300e+01
#> Long1|etaslope  -1.264015e+12  1.549000e+00  0.000000e+00
#> Long2|etaauc     3.360000e-01  0.000000e+00  1.399000e+00
#> b-splines-coef1  0.000000e+00  0.000000e+00            NA
#> b-splines-coef2  0.000000e+00  0.000000e+00            NA
#> b-splines-coef3  0.000000e+00  0.000000e+00            NA
#> b-splines-coef4  0.000000e+00  0.000000e+00            NA
#> b-splines-coef5  0.000000e+00  0.000000e+00            NA
#> b-splines-coef6  0.000000e+00  0.000000e+00            NA
#> 
#> Group-level error terms:
#>  Groups Name              Std.Dev. Corr     
#>  id     Long1|(Intercept) 1.11688           
#>         Long2|(Intercept) 0.36463  0.00     
#>         Long2|year        0.03419  0.00 0.00
#> Num. levels: id 40 
#> 
#> Sample avg. posterior predictive distribution 
#> of longitudinal outcomes:
#>                Median MAD_SD
#> Long1|mean_PPD 0.885  0.036 
#> Long2|mean_PPD 3.145  0.021 
#> 
#> ------
#> For info on the priors used see help('prior_summary.stanreg').Fitting a multivariate joint model.
#> 
#> Please note the warmup may be much slower than later iterations!
#> 
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000363 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.63 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1:          three stages of adaptation as currently configured.
#> Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1:          the given number of warmup iterations:
#> Chain 1:            init_buffer = 7
#> Chain 1:            adapt_window = 38
#> Chain 1:            term_buffer = 5
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 3.401 seconds (Warm-up)
#> Chain 1:                7.981 seconds (Sampling)
#> Chain 1:                11.382 seconds (Total)
#> Chain 1: 
#> Warning: The largest R-hat is 1.14, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Fitting a multivariate joint model.
#> 
#> Please note the warmup may be much slower than later iterations!
#> 
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000287 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.87 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 16.082 seconds (Warm-up)
#> Chain 1:                7.295 seconds (Sampling)
#> Chain 1:                23.377 seconds (Total)
#> Chain 1: 
#> Warning: The largest R-hat is 1.06, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess