BETA: Covariate adjustment using working models from the super learner libraries through the AIPW package with cross-fitting, with median adjustment.

Estimate treatment-group-specific response means and (optionally) treatment group contrasts using a generalized linear working model. Perform median adjustment to limit randomness induced from cross-fitting.

robincar_SL_median(
  n_times,
  seed,
  df,
  treat_col,
  response_col,
  car_strata_cols = NULL,
  covariate_cols = NULL,
  car_scheme = "simple",
  covariate_to_include_strata = NULL,
  SL_libraries = c(),
  SL_learners = c(),
  k_split = 2,
  g_accuracy = 7,
  contrast_h = NULL,
  contrast_dh = NULL
)

Arguments

n_times

Number of times to run the robincar_SL function

seed

Seed to set before running the set of functions

df

A data.frame with the required columns

treat_col

Name of column in df with treatment variable

response_col

Name of the column in df with response variable

car_strata_cols

Names of columns in df with car_strata variables

covariate_cols

Names of columns in df with covariate variables

car_scheme

Name of the type of covariate-adaptive randomization scheme. One of: "simple", "pocock-simon", "biased-coin", "permuted-block".

covariate_to_include_strata

Whether to include car_strata variables in covariate adjustment. Defaults to F for ANOVA and ANCOVA; defaults to T for ANHECOVA. User may override by passing in this argument.

SL_libraries

Vector of super-learner libraries to use for the covariate adjustment (see SuperLearner::listWrappers)

SL_learners

Optional list of super-learner "learners" to use for the covariate adjustment (see SuperLearner::create.Learner())

k_split

Number of splits to use in cross-fitting

g_accuracy

Level of accuracy to check prediction un-biasedness (in digits).

contrast_h

An optional function to specify a desired contrast

contrast_dh

An optional jacobian function for the contrast (otherwise use numerical derivative)

Value

See value of RobinCar::robincar_SL. Attributes `mods` and `mu_as` are lists of `mod` and `mu_a` attributes, respectively, for each replicate of `robincar_SL` used in the median.

Details

*WARNING: This function is still under development and has not been extensively tested.* This function currently only works for two treatment groups. Before using this function, you must load the SuperLearner library with `library(SuperLearner)`, otherwise the function call will fail.