This function calculates effect sizes for each variable in a data frame, comparing treatment and control groups. It handles continuous, binary, and categorical variables using appropriate effect size measures.
calculateEffectSizes(data, treatment_column, to_check = NULL)A data frame with two columns:
* Variable: The name of each variable in the original data frame.
* EffectSize: The calculated effect size for each variable.
A data frame containing the variables and treatment/control indicator.
The name of the column (as a string) in the data frame that indicates whether an observation is in the treatment or control group. This column must be a factor with exactly two levels.
(optional) A character vector specifying the names of the variables for which effect sizes should be calculated. If NULL (default), all variables (except the treatment column) are processed.
- For continuous variables, Hedges' g effect size is calculated. - For binary variables, Cox's Proportional Hazards Index (Cox's C) is calculated. - For categorical variables, the variable is converted into multiple indicator (dummy) variables, and the average Cox's C across these indicators is reported.