riskreg_cens: Binary regression models with right censored outcomes

In targeted: Targeted Inference

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

targeted documentation built on May 29, 2024, 2:08 a.m.