plot_hazard_ratio: Plot hazard ratio against time from a survextrap model
In survextrap: Bayesian Flexible Parametric Survival Modelling and Extrapolation

plot_hazard_ratio

R Documentation

Plot hazard ratio against time from a survextrap model

Description

For use with non-proportional hazards models (survextrap(...,nonprop=TRUE)). Intended as a quick check of a model fit, so there are limited customisation options. The underlying data can be extracted with hazard_ratio.

Usage

plot_hazard_ratio(
  x,
  newdata = NULL,
  t = NULL,
  tmax = NULL,
  niter = NULL,
  ci = TRUE,
  xlab = "Time",
  ylab = "Hazard ratio",
  line_size = 1.5,
  ci_alpha = 0.2
)

Arguments

`x`	A fitted model object as returned by `survextrap`
`newdata`	A data frame with two rows. The hazard ratio will be defined as hazard(second row) divided by hazard(first row). If the only covariate in the model is a factor with two levels, then `newdata` defaults to a data frame containing the levels of this factor, so that the hazard ratio for the second level versus the first level is computed. For any other models, `newdata` must be supplied explicitly. Standardisation (with `standardise_to`) is not supported. This might be done by hand by using `hazard(...,sample=TRUE)` to obtain posterior samples for the two standardised hazards separately, then summarising by hand.
`t`	Vector of times at which to compute the estimates.
`tmax`	Maximum time at which to compute the estimates. If `t` is supplied, then this is ignored. If `t` is not supplied, then `t` is set to a set of 100 equally spaced time points from 0 to `tmax`. If both `tmax` and `t` are not supplied, then `tmax` is set to the maximum follow up time in the data.
`niter`	Number of MCMC iterations to use to compute credible intervals. Set to a low value to make this function quicker, at the cost of some approximation error (which may not be important for plotting or model development).
`ci`	If `TRUE` then credible intervals are drawn. Defaults to drawing the intervals if the plot shows the curve for only one covariate value.
`xlab`	X-axis label
`ylab`	Y-axis label
`line_size`	Passed to `geom_line`
`ci_alpha`	Transparency for the credible interval ribbons