MARMoT {MARMoT}

MARMoT balancing method

Description

Matching on poset-based average rank for multiple treatments (MARMoT).

Usage

MARMoT(
  data,
  confounders,
  treatment,
  reference = "median",
  n.cores = 1,
  caliper = 0.25,
  verbose = TRUE
)

Arguments

data

A dataframe or equivalent.

confounders

A vector containing the column names of the confounders to balance by.

treatment

A string indicating the column name of the treatment variable.

reference

The statistic used to determine the reference frequencies in the balancing process. Default is median.

n.cores

Number of cores to be used (Linux and Mac systems only!); if a number grater than 1 is specified the function will use a parallelized version of the deloof approximation. Default set to 1.

caliper

Fraction of the standard deviation used to determine the closest neighbour. Default is 0.25.

verbose

Set to FALSE to suppress any console output. Default is TRUE

Details

There are many scenarios where classic propensity score techniques are not applicable (e.g. there are many treatments). In a multiple-treatment framework, MARMoT is a method to balance the distribution of covariates among several treatment groups. MARMoT introduces a method for achieving balance among treatment groups by utilizing partially ordered set (poset) theory. This approach focuses on equalizing individual characteristics without relying on propensity score techniques or a dependent variable. Unlike propensity score methods, poset theory doesn't require assumptions about model specifications for treatment allocation. Each subject is represented by a profile of their characteristics, and an average rank approximation is associated with each profile. This value represents the significance of individual characteristics for treatment allocation and can be normalized for better interpretability.

Value

A list of objects, also containing the balanced dataset with the same structure of the input dataset.

References

Silan, M., Boccuzzo, G. and Arpino, B., 2021. 'Matching on poset‐based average rank for multiple treatments to compare many unbalanced groups'. Statistics in Medicine, 40(28), pp.6443-6458. https://doi.org/10.1002/sim.9192

Silan, M., Belloni, P. and Boccuzzo, G., 2023. 'Identification of neighborhood clusters on data balanced by a poset-based approach'. Statistical Methods & Applications, pp.1-22. https://doi.org/10.1007/s10260-023-00695-0

Examples

out = MARMoT(data = MARMoT_data, confounders = c("race", "age"),
             treatment = "hospital", n.cores = 1)
out