robincar_linear {RobinCar}

Covariate adjustment using linear working model

Description

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

Usage

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)

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.

Value

See value of RobinCar::robincar_glm(), this function is a wrapper using a linear link function.