Binary regression models with right censored outcomes

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