estimateQ1.ltmle: estimateQ1.ltmle

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

estimateQ1.ltmle

Description

This function computes the conditional treatment probabilities at time 1

Usage

estimateQ1.ltmle(L2, Q2n, L0, A0, A1, folds, validFold, abar, SL.Q,
  SL.Q.options, glm.Q, glm.Q.options, return.models, verbose, stratify, ...)

Arguments

L2	A vector outcome of interest
L0	A data.frame featuring covariates measured at baseline.
A0	A vector treatment delivered at baseline.
A1	A vector treatment deliver after L1 is measured.
folds	Vector of cross-validation folds
validFold	Which fold is validation fold
abar	A vector of length 2 indicating the treatment assignment that is of interest.
SL.Q	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.Q.options	A list of additional arguments passed to SuperLearner for outcome regression fits.
glm.Q	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.Q = NULL.
glm.Q.options	A list of additional arguments passed to glm for the outcome regression fits. Typically, the family argument.
return.models	A boolean indicating whether the models for g00 should be returned with the output.
stratify	A boolean indicating whether to pool across treatment nodes or to estimate outcome regression separately in each category.
...	Other arguments (not currently used).
L1	A data.frame featuring time-varying covariates measured at the first timepoint.

Value

Returns a list with Q2n, Q1n, and the estimated model objects if return.models = TRUE

Examples

TO DO : add examples

benkeser/drinf documentation built on Oct. 22, 2023, 9:50 a.m.