estimateQ: estimateQ

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

estimateQ

Description

This function computes the conditional treatment probabilities at both timepoints.

Usage

estimateQ(validFold, folds, L0, L1, L2, A0, A1, abar, SL.Q, SL.Q.options, glm.Q,
  glm.Q.options, return.models, verbose, stratify, ...)

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.
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).

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.