rstanarm: plot.survfit.stanjm – R documentation

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plot.survfit.stanjm

Plot the estimated subject-specific or marginal survival function

Description

This generic plot method for survfit.stanjm objects will plot the estimated subject-specific or marginal survival function using the data frame returned by a call to posterior_survfit. The call to posterior_survfit should ideally have included an "extrapolation" of the survival function, obtained by setting the extrapolate argument to TRUE.

The plot_stack_jm function takes arguments containing the plots of the estimated subject-specific longitudinal trajectory (or trajectories if a multivariate joint model was estimated) and the plot of the estimated subject-specific survival function and combines them into a single figure. This is most easily understood by running the Examples below.

Usage

## S3 method for class 'survfit.stanjm'
plot(
  x,
  ids = NULL,
  limits = c("ci", "none"),
  xlab = NULL,
  ylab = NULL,
  facet_scales = "free",
  ci_geom_args = NULL,
  ...
)

plot_stack_jm(yplot, survplot)

Arguments

`x`	A data frame and object of class `survfit.stanjm` returned by a call to the function `posterior_survfit`. The object contains point estimates and uncertainty interval limits for estimated values of the survival function.
`ids`	An optional vector providing a subset of subject IDs for whom the predicted curves should be plotted.
`limits`	A quoted character string specifying the type of limits to include in the plot. Can be one of: `"ci"` for the Bayesian posterior uncertainty interval for the estimated survival probability (often known as a credible interval); or `"none"` for no interval limits.
`xlab, ylab`	An optional axis label passed to `labs`.
`facet_scales`	A character string passed to the `scales` argument of `facet_wrap` when plotting the longitudinal trajectory for more than one individual.
`ci_geom_args`	Optional arguments passed to `geom_ribbon` and used to control features of the plotted interval limits. They should be supplied as a named list.
`...`	Optional arguments passed to `geom_line` and used to control features of the plotted survival function.
`yplot`	An object of class `plot.predict.stanjm`, returned by a call to the generic `plot` method for objects of class `predict.stanjm`. If there is more than one longitudinal outcome, then a list of such objects can be provided.
`survplot`	An object of class `plot.survfit.stanjm`, returned by a call to the generic `plot` method for objects of class `survfit.stanjm`.

Value

The plot method returns a ggplot object, also of class plot.survfit.stanjm. This object can be further customised using the ggplot2 package. It can also be passed to the function plot_stack_jm.

plot_stack_jm returns an object of class bayesplot_grid that includes plots of the estimated subject-specific longitudinal trajectories stacked on top of the associated subject-specific survival curve.

Examples

# Run example model if not already loaded
  if (!exists("example_jm")) example(example_jm)
  
  # Obtain subject-specific conditional survival probabilities
  # for all individuals in the estimation dataset.
  ps1 <- posterior_survfit(example_jm, extrapolate = TRUE)
  
  # We then plot the conditional survival probabilities for
  # a subset of individuals
  plot(ps1, ids = c(7,13,15))
  # We can change or add attributes to the plot
  plot(ps1, ids = c(7,13,15), limits = "none")
  plot(ps1, ids = c(7,13,15), xlab = "Follow up time")
  plot(ps1, ids = c(7,13,15), ci_geom_args = list(fill = "red"),
       color = "blue", linetype = 2)
  plot(ps1, ids = c(7,13,15), facet_scales = "fixed")
  
  # Since the returned plot is also a ggplot object, we can
  # modify some of its attributes after it has been returned
  plot1 <- plot(ps1, ids = c(7,13,15))
  plot1 + 
    ggplot2::theme(strip.background = ggplot2::element_blank()) +
    ggplot2::coord_cartesian(xlim = c(0, 15)) +
    ggplot2::labs(title = "Some plotted survival functions")
    
  # We can also combine the plot(s) of the estimated 
  # subject-specific survival functions, with plot(s) 
  # of the estimated longitudinal trajectories for the
  # same individuals
  ps1 <- posterior_survfit(example_jm, ids = c(7,13,15))
  pt1 <- posterior_traj(example_jm, , ids = c(7,13,15))
  plot_surv <- plot(ps1) 
  plot_traj <- plot(pt1, vline = TRUE, plot_observed = TRUE)
  plot_stack_jm(plot_traj, plot_surv)
   
  # Lastly, let us plot the standardised survival function
  # based on all individuals in our estimation dataset
  ps2 <- posterior_survfit(example_jm, standardise = TRUE, times = 0,
                          control = list(epoints = 20))
  plot(ps2)   

   

  if (!exists("example_jm")) example(example_jm)
  ps1 <- posterior_survfit(example_jm, ids = c(7,13,15))
  pt1 <- posterior_traj(example_jm, ids = c(7,13,15), extrapolate = TRUE)
  plot_surv <- plot(ps1) 
  plot_traj <- plot(pt1, vline = TRUE, plot_observed = TRUE)
  plot_stack_jm(plot_traj, plot_surv)

rstanarm

Bayesian Applied Regression Modeling via Stan

v2.21.1

GPL (>= 3)

Authors

Jonah Gabry [aut], Imad Ali [ctb], Sam Brilleman [ctb], Jacqueline Buros Novik [ctb] (R/stan_jm.R), AstraZeneca [ctb] (R/stan_jm.R), Trustees of Columbia University [cph], Simon Wood [cph] (R/stan_gamm4.R), R Core Deveopment Team [cph] (R/stan_aov.R), Douglas Bates [cph] (R/pp_data.R), Martin Maechler [cph] (R/pp_data.R), Ben Bolker [cph] (R/pp_data.R), Steve Walker [cph] (R/pp_data.R), Brian Ripley [cph] (R/stan_aov.R, R/stan_polr.R), William Venables [cph] (R/stan_polr.R), Paul-Christian Burkner [cph] (R/misc.R), Ben Goodrich [cre, aut]

Initial release

2020-07-20