robincar_SL: BETA: Covariate adjustment using working models from the...

In RobinCar: Robust Estimation and Inference in Covariate-Adaptive Randomization

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

Description

Estimate treatment-group-specific response means and (optionally) treatment group contrasts using a generalized linear working model.

Usage

robincar_SL(
  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

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)

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.

Value

See value of RobinCar::robincar_glm, but the working model for \hat{\mu}(X_i) is based on the AIPW package that uses specified SuperLearner libraries and cross-fitting. Also, 'mod' attribute is an object of class AIPW::AIPW.

Examples

library(SuperLearner)
library(ranger)
n <- 1000
set.seed(10)
DATA2 <- data.frame(A=rbinom(n, size=1, prob=0.5),
                    y=rbinom(n, size=1, prob=0.2),
                    x1=rnorm(n),
                    x2=rnorm(n),
                    x3=as.factor(rbinom(n, size=1, prob=0.5)),
                    z1=rbinom(n, size=1, prob=0.5),
                    z2=rbinom(n, size=1, prob=0.5))
DATA2[, "y"] <- NA
As <- DATA2$A == 1
DATA2[DATA2$A == 1, "y"] <- rbinom(
  sum(As),
  size=1,
  prob=exp(DATA2[As,]$x1)/(1+exp(DATA2[As,]$x1)))
DATA2[DATA2$A == 0, "y"] <- rbinom(
  n-sum(As),
  size=1,
  prob=exp(1 +
    5*DATA2[!As,]$x1 + DATA2[!As,]$x2)/
    (1+exp(1 + 5*DATA2[!As,]$x1 + DATA2[!As,]$x2)))
DATA2$A <- as.factor(DATA2$A)

sl.mod <- robincar_SL(
  df=DATA2,
  response_col="y",
  treat_col="A",
  car_strata_cols=c("z1"),
  covariate_cols=c("x1"),
  SL_libraries=c("SL.ranger"),
  car_scheme="permuted-block",
  covariate_to_include_strata=TRUE
)

sl.mod$result

RobinCar documentation built on May 29, 2024, 3:03 a.m.