Add transition probabilities — add_trans

add_trans_prob adds transition probabilities on the provided data set and model. Optionally, confidence intervals (CI) are added if ci=TRUE. The function builds on cumulative hazards cumu_hazard and mgcv::gam models.

add_trans_prob(
  newdata,
  object,
  overwrite = FALSE,
  ci = FALSE,
  alpha = 0.05,
  nsim = 100L,
  time_var = "tend",
  interval_length = "intlen",
  transition = "transition",
  ...
)

Arguments

newdata: A data frame or list containing the values of the model covariates at which predictions are required. If this is not provided then predictions corresponding to the original data are returned. If newdata is provided then it should contain all the variables needed for prediction: a warning is generated if not. See details for use with linear.functional.terms.
object: A fitted gam object as produced by mgcv::gam
overwrite: Should transition probability columns be overwritten if already present in the data set? Defaults to FALSE. If TRUE, columns with names c("trans_prob", "trans_upper", "trans_lower") will be overwritten.
ci: Logical, defaults to TRUE. Decides if confidence intervals for transition probabilities are calculated.
alpha: Sets the confidence intervals' \(\alpha\) level, Defaults to 0.05
nsim: Sets the number of iterations for simulated confidence intervals. Defaults to 100L
time_var: Name of the variable used for the baseline hazard. Defaults to "tend".
interval_length: Character, defaults to "intlen". contains the interval length in newdata.
transition: Character, defaults to "transition". contains the transition labels in newdata.
...: Further arguments passed to underlying methods.

Details

When computing transition probabilities for multiple groups, the input data must be grouped via group_by() before calling this function. Omitting group_by() will not produce an error or warning but will return silently incorrect results, as the transition probability will be accumulated over the entire dataset rather than within each group.

Examples

  data("prothr", package = "mstate")
  prothr <- prothr |>
    mutate(transition = as.factor(paste0(from, "->", to))
    , treat = as.factor(treat)) |>
    filter(Tstart != Tstop, id <= 100) |> select(-trans)
  ped <- as_ped(data= prothr, formula= Surv(Tstart, Tstop, status)~ .,
    transition = "transition", id= "id", timescale  = "calendar")
  pam <- mgcv::bam(ped_status ~ s(tend, by=transition) + transition * treat,
    data = ped, family = poisson(), offset = offset,
    method = "fREML", discrete = TRUE)
  ndf <- make_newdata(ped, tend  = unique(tend),
    treat  = unique(treat),
    transition = unique(transition)) |>
    group_by(treat, transition) |>  # important!
    add_trans_prob(pam)