Hazard ratio against time in a survextrap model — hazard

Compute the hazard ratio at a series of time points, estimated from a survextrap model. Intended for use with non-proportional hazards models (survextrap(...,nonprop=TRUE)). In proportional hazards models (which survextrap fits by default) the hazard ratio is constant with time.

Usage

hazard_ratio(
  x,
  newdata = NULL,
  t = NULL,
  tmax = NULL,
  niter = NULL,
  summ_fns = NULL,
  sample = FALSE
)

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.
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).
summ_fns: A list of functions to use to summarise the posterior sample. This is passed to posterior::summarise_draws. By default this is list(median=median, ~quantile(.x, probs=c(0.025, 0.975))). If the list is named, then the names will be used for the columns of the output.
sample: If TRUE then the MCMC samples are returned instead of being summarised as a median and 95% credible intervals.

Value

A data frame (tibble) with each row containing posterior summary statistics for different times.

Or if sample=TRUE, an array with dimensions length(t), niter, and 1, giving the incremental RMST evaluated at different times and MCMC iterations respectively.