Covariate adjustment using linear working model, with simplified interface.

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

robincar_linear2(
  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)

Details

Note the difference between `robincar_linear` and `robincar_linear2` with regards to their `covariate_cols` argument.