surv_onestep_complete: One-step TMLE estimator for survival curve (No censoring)

In wilsoncai1992/onestep.survival: One-step TMLE for survival analysis

Description

options to ADD: SL.formula: the covariates to include in SL

Usage

surv_onestep_complete(dat, dW, g.SL.Lib = c("SL.glm", "SL.step",
  "SL.glm.interaction"), ht.SL.Lib = c("SL.mean", "SL.glm", "SL.gam",
  "SL.earth"), ..., epsilon.step = 1e-05, max.iter = 1000,
  tol = 1/nrow(dat), T.cutoff = NULL, verbose = TRUE)

Arguments

dat	data.frame with columns T, A, W. All columns with character "W" will be treated as baseline covariates.
dW	binary input vector specifying dynamic treatment (as a function output of W)
g.SL.Lib	SuperLearner library for fitting treatment regression
ht.SL.Lib	SuperLearner library for fitting conditional hazard regression
...	additional options for plotting initial fit curve
epsilon.step	step size for one-step recursion
max.iter	maximal number of recursion for one-step
tol	tolerance for optimization
T.cutoff	manual right censor the data; remove parts dont want to esimate
verbose	to plot the initial fit curve and the objective function value during optimzation

Value

Psi.hat vector of survival curve under intervention

T.uniq vector of time points where Psi.hat gets values (have same length as Psi.hat)

params list of meta-information of estimation

variables list of data summary

initial_fit list of initial fit (hazard, g_1, Delta)

wilsoncai1992/onestep.survival documentation built on May 29, 2019, 11:58 a.m.