New multivariate tests for assessing covariate balance in matched observational studies

We propose new tests for assessing whether covariates in a treatment group and matched control group are balanced in observational studies. The tests exhibit high power under a wide range of multivariate alternatives, some of which existing tests have little power for. The asymptotic permutation null distributions of the proposed tests are studied and the P-values calculated through the asymptotic results work well in simulation studies, facilitating the application of the test to large data sets. The tests are illustrated in a study of the effect of smoking on blood lead levels. The proposed tests are implemented in an R package BalanceCheck.

Automated summary

This paper proposes new tests to assess the balance of covariates between treatment and control groups in observational studies, with higher power and better asymptotic properties than existing methods.