riskreg_cens {targeted}

Binary regression models with right censored outcomes

Description

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 (ml_model)

prediction

Optional prediction model (ml_model)

data

data.frame.

newdata

Optional data.frame. In this case the uncentered influence function evalued in 'newdata' is returned with nuisance parameters obtained from 'data'.

tau

Time-point of interest, see Details.

type

"risk", "treatment", "rmst", "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.

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.##'

Value

estimate object

Author(s)

Klaus K. Holst, Andreas Nordland