generalize: Generalize Average Treatment Effect from Randomized Trial to...

In katiecoburn/generalizeR: What the Package Does (One Line, Title Case)

Description

The method used to generalize average treatment effect to the target population. Default is "weighting" (weighting by participation probability)

Usage

generalize(
  outcome,
  treatment,
  trial,
  selection_covariates,
  data,
  selection_method = "lr",
  is_data_disjoint = TRUE,
  trim_pop = FALSE,
  seed
)

Arguments

outcome	variable name denoting outcome
treatment	variable name denoting binary treatment assignment (okay 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
selection_method	method to estimate the probability of trial participation. Default is logistic regression ("lr"). Other methods supported are Random Forests ("rf") and Lasso ("lasso")
is_data_disjoint	logical. If TRUE, then trial and population data are considered independent. This affects calculation of the weights - see details for more information.
trim_pop	logical. If TRUE, then population data are subset to exclude individuals with covariates outside bounds of trial covariates.
seed	numeric. By default, the seed is set to 13783, otherwise can be specified (such as for simulation purposes).

Value

generalize returns an object of the class "generalize"

katiecoburn/generalizeR documentation built on Oct. 28, 2020, 4:43 a.m.