Covariate adjustment using linear working model — robincar

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

robincar_linear(
  df,
  treat_col,
  response_col,
  car_strata_cols = NULL,
  covariate_cols = NULL,
  car_scheme = "simple",
  adj_method = "ANOVA",
  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. **If you want to include the strata variables as covariates also, add them here.**
car_scheme: Name of the type of covariate-adaptive randomization scheme. One of: "simple", "pocock-simon", "biased-coin", "permuted-block".
adj_method: Name of linear adjustment method to use. One of: "ANOVA", "ANCOVA", "ANHECOVA".
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_glm(), this function is a wrapper using a linear link function.

Details

* Adjustment method "ANOVA" fits a linear model with formula `Y ~ A` where `A` is the treatment group indicator and `Y` is the response. * "ANCOVA" fits a linear model with `Y ~ A + X` where `X` are the variables specified in the `covariate_cols` argument. * "ANHECOVA" fits a linear model with `Y ~ A*X`, the main effects and treatment-by-covariate interactions.