A Matching Algorithm for Designs with Multiple Groups.

Polymatching: Matching in Designs with Multiple Treatment Groups

Description

The package implements the conditionally optimal matching algorithm, which can be used to generate matched samples in designs with multiple treatment groups.

Details

Currently, the algorithm can be applied to datasets with 3 to 6 groups and generates matched samples with one subject per group. The package provides functions to generate the matched sample and to evaluate the balance in key covariates.

Generating the Matched Sample

The function implementing the matching algorithm is polymatch. The algorithm is iterative and needs a matched sample with one subject per group as starting point. This matched sample can be automatically generated by polymatch or can be provided by the user. The algorithm iteratively explores possible reductions in the total distance of the matched sample.

Evaluating Balance in Covariates

Balance in key covariates can be evaluated with the function balance. Given a matched sample and a set of covariates of interest, the function computes the standardized differences and the ratio of the variances for each pair of treatment groups in the study design. For 3, 4, 5 and 6 groups, there are 3, 6, 10 and 15 pairs of groups and the balance is evaluated before and after matching. The result of balance can be graphically represented with plotBalance.

Installation

You can install the package with the function install_github of the package devtools.

library(devtools)
install_github("gnattino/polymatching")