R: A function for estimating the proportion of treatment effect...

xfr_surrogate

R Documentation

A function for estimating the proportion of treatment effect explained using repeated cross-fitting.

Description

A function for estimating the proportion of treatment effect explained using repeated cross-fitting.

Usage

xfr_surrogate(
  ds,
  x = NULL,
  s,
  y,
  a,
  splits = 50,
  K = 5,
  outcome_learners = NULL,
  ps_learners = NULL,
  interaction_model = TRUE,
  trim_at = 0.05,
  outcome_family = gaussian(),
  mthd = "superlearner",
  n_ptb = 0,
  ...
)

Arguments

`ds`	a `data.frame`.
`x`	names of all covariates in `ds` that should be included to control for confounding (eg. age, sex, etc). Default is `NULL`.
`s`	names of surrogates in `ds`.
`y`	name of the outcome in `ds`.
`a`	treatment variable name (eg. groups). Expect a binary variable made of `1`s and `0`s.
`splits`	number of data splits to perform.
`K`	number of folds for cross-fitting. Default is `5`.
`outcome_learners`	string vector indicating learners to be used for estimation of the outcome function (e.g., `"SL.ridge"`). See the SuperLearner package for details.
`ps_learners`	string vector indicating learners to be used for estimation of the propensity score function (e.g., `"SL.ridge"`). See the SuperLearner package for details.
`interaction_model`	logical indicating whether outcome functions for treated and control should be estimated separately. Default is `TRUE`.
`trim_at`	threshold at which to trim propensity scores. Default is `0.05`.
`outcome_family`	default is `'gaussian'` for continuous outcomes. Other choice is `'binomial'` for binary outcomes.
`mthd`	selected regression method. Default is `'superlearner'`, which uses the `SuperLearner` package for estimation. Other choices include `'lasso'` (which uses `glmnet`), `'sis'` (which uses `SIS`), `'cal'` (which uses `RCAL`).
`n_ptb`	Number of perturbations. Default is `0` which means asymptotic standard errors are used.
`...`	additional parameters (in particular for super_learner)

Value

a tibble with columns:

Rm: estimate of the proportion of treatment effect explained, computed as the median over the repeated splits.
R_se0 standard error for the PTE, accounting for the variability due to splitting.
R_cil0 lower confidence interval value for the PTE.
R_cih0 upper confidence interval value for the PTE.
Dm: estimate of the overall treatment effect, computed as the median over the repeated splits.
D_se0 standard error for the overall treatment effect, accounting for the variability due to splitting.
D_cil0 lower confidence interval value for the overall treatment effect.
D_cih0 upper confidence interval value for the overall treatment effect.
Dsm: estimate of the residual treatment effect, computed as the median over the repeated splits.
Ds_se0 standard error for the residual treatment effect, accounting for the variability due to splitting.
Ds_cil0 lower confidence interval value for the residual treatment effect.
Ds_cih0 upper confidence interval value for the residual treatment effect.

Examples


n <- 100
p <- 20
q <- 2
wds <- sim_data(n = n, p = p)

if(interactive()){
 lasso_est <- xfr_surrogate(ds = wds,
   x = paste('x.', 1:q, sep =''),
   s = paste('s.', 1:p, sep =''),
   a = 'a',
   y = 'y',
   splits = 2,
   K = 2,
   trim_at = 0.01,
   mthd = 'lasso',
   ncores = 1)
}