drinf.tmle: drinf.tmle

In benkeser/drinf: Computes doubly-robust estimators and doubly-robust confidence intervals

drinf.tmle

Description

This function computes the time-varying covariate-adjusted mean of an outcome under a specified treatment assignment using targeted minimum loss-based estimation.

Usage

drinf.tmle(L0, L1, L2, A0, A1, abar = c(1, 1), stratify = TRUE,
  SL.Q = NULL, SL.g = NULL, SL.Qr = NULL, SL.gr = NULL, glm.Q = NULL,
  glm.g = NULL, guard = c("Q", "g"), universal = FALSE,
  universalStepSize = 1e-04, printFreq = 50, flucOrd = c("targetQ2",
  "targetQ1", "targetg1", "targetg0"), return.models = FALSE, maxIter = 20,
  tolIF = 1/(length(L2)), tolg = 1e-08, tolQ = 1e-08, verbose = TRUE,
  SL.Q.options = list(family = gaussian()), SL.g.options = list(family =
  binomial()), glm.Q.options = list(family = gaussian()),
  return.ltmle = TRUE, only.ltmle = FALSE, return.naive = TRUE,
  cvFolds = 1, ...)

Arguments

L0	A data.frame featuring covariates measured at baseline.
L1	A data.frame featuring time-varying covariates measured at the first timepoint.
L2	A vector outcome of interest
A0	A vector treatment delivered at baseline.
A1	A vector treatment deliver after L1 is measured.
abar	A vector of length 2 indicating the treatment assignment that is of interest.
stratify	A boolean indicating whether to pool across treatment nodes or to estimate outcome regression separately in each category. Should be kept TRUE until I have more time to think about how to pool across treatment arms?
SL.Q	A vector or list specifying the SuperLearner library to be used to estimate the outcome regressions at each time point. See SuperLearner package for details.
SL.g	A vector or list specifying the SuperLearner library to be used to estimate the conditional probability of treatment at each time point. See SuperLearner package for details.
SL.Qr	A vector or list specifying the SuperLearner library to be used to estimate the reduced-dimension regression to protect against misspecification of the outcome regressions. See SuperLearner package for details.
SL.gr	A vector or list specifying the SuperLearner library to be used to estimate the reduced-dimension regression to protect against misspecification of the conditional treatment probabilities. See SuperLearner package for details.
glm.Q	A character specifying the right-hand side of the glm formula used to estimate the outcome regressions at each time point. Only used if SL.Q = NULL.
glm.g	A character specifying the right-hand side of the glm formula used to estimate the conditional probability of treatment at each time point. Only used if SL.g = NULL.
guard	A vector of characters, either "Q", "g", both, or neither (NULL). Indicates whether to guard against misspecification of outcome or treatment regressions or both. Currently only works with c("Q","g").
universal	A boolean indicating whether to perform TMLE step using locally least favorable parametric submodels (if FALSE) or universally least favorable submodels (if TRUE)
universalStepSize	A numeric indicating the step size for the recursive calculation of universally least favorable submodel. Default is 0.005.
return.models	A boolean indicating whether the models for Q, g, Qr, and gr should be returned with the output.
maxIter	A numeric indicating the maximum number of TMLE iterations before stopping.
tolIF	A numeric stopping criteria for the TMLE updates based on the empirical average of the estimated influence curve.
tolg	A numeric indicating the truncation level for conditional treatment probabilities.
tolQ	A numeric indicating the truncation level for transformed outcome regressions.
verbose	A boolean indicating whether messages should be printed to indicate progress.
SL.Q.options	A list of additional arguments passed to SuperLearner for outcome regression fits.
SL.g.options	A list of additional arguments passed to SuperLearner for condtional treatment probability fits.
glm.Q.options	A list of additional arguments passed to glm for outcome regression fits.
return.ltmle	A boolean indicating whether to compute the LTMLE estimate using a similar iterative updating scheme.
only.ltmle	Only return ltmle (for bootstrapping)
return.naive	A boolean indicating whether to return the naive plug-in estimate.
...	Other arguments (not currently used)

Value

TO DO: Add return values

Examples

TO DO : Add Examples

benkeser/drinf documentation built on June 10, 2025, 11:25 p.m.