generalize_tmle: TMLE for estimating TATE

In benjamin-ackerman/generalize: Generalizing Average Treatment Effects from RCTs to Target Populations

Description

TMLE for estimating TATE

Usage

generalize_tmle(
  outcome,
  treatment,
  trial,
  selection_covariates,
  data,
  is_data_disjoint = TRUE,
  seed
)

Arguments

outcome	variable name denoting outcome
treatment	variable name denoting binary treatment assignment (ok if only available in trial, not population)
trial	variable name denoting binary trial participation (1 = trial participant, 0 = not trial participant)
selection_covariates	vector of covariate names in data set that predict trial participation
data	data frame comprised of "stacked" trial and target population data
is_data_disjoint	logical. If TRUE, then trial and population data are considered independent.
seed	numeric. By default, the seed is set to 13783, otherwise can be specified (such as for simulation purposes).

Value

generalize_tmle returns a list of the TATE estimate, standard error, and 95% CI bounds

benjamin-ackerman/generalize documentation built on Oct. 11, 2020, 3:58 a.m.