Binary regression models with right censored outcomes

Usage

riskreg_cens(
  response,
  censoring,
  treatment = NULL,
  prediction = NULL,
  data,
  newdata,
  tau,
  type = "risk",
  M = 1,
  call.response = "phreg",
  args.response = list(),
  call.censoring = "phreg",
  args.censoring = list(),
  preprocess = NULL,
  efficient = TRUE,
  control = list(),
  ...
)

Arguments

response: Response formula (e.g., Surv(time, event) ~ D + W).
censoring: Censoring formula (e.g., Surv(time, event == 0) ~ D + A + W)).
treatment: Optional treatment model (learner)
prediction: Optional prediction model (learner)
data: data.frame.
newdata: Optional data.frame. In this case the uncentered influence function evaluated in 'newdata' is returned with nuisance parameters obtained from 'data'.
tau: Time-point of interest, see Details.
type: "risk", "treatment", "brier"
M: Number of folds in cross-fitting (M=1 is no cross-fitting).
call.response: Model call for the response model (e.g. "mets::phreg").
args.response: Additional arguments to the response model.
call.censoring: Similar to call.response.
args.censoring: Similar to args.response.
preprocess: (optional) Data pre-processing function.
efficient: If FALSE an IPCW estimator is returned
control: See details
...: Additional arguments to lower level data pre-processing functions.

Value

estimate object

Details

The one-step estimator depends on the calculation of an integral wrt. the martingale process corresponding to the counting process N(t) = I(C>min(T,tau)). This can be decomposed into an integral wrt the counting process, \(dN_c(t)\) and the compensator \(d\Lambda_c(t)\) where the latter term can be computational intensive to calculate. Rather than calculating this integral in all observed time points, we can make a coarser evaluation which can be controlled by setting control=(sample=N). With N=0 the (computational intensive) standard evaluation is used.

Author

Klaus K. Holst, Andreas Nordland