drinf.tmle: drinf.tmle

Description Usage Arguments Value Examples

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

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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

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TO DO : Add Examples

benkeser/drinf documentation built on May 12, 2019, 11:59 a.m.