polymatching {polymatching} | R Documentation |
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 up to 10 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
.
Author(s)
Maintainer: Giovanni Nattino giovanni.nattino@marionegri.it
Authors:
Bo Lu
Chi Song
Henry Xiang