Matching in cluster randomized trials using the Goldilocks Approach

S Gwynn Sturdevant; Susan S Huang; Richard Platt; Ken Kleinman

doi:10.1016/j.conctc.2021.100746

Matching in cluster randomized trials using the Goldilocks Approach

Contemp Clin Trials Commun. 2021 May 5:22:100746. doi: 10.1016/j.conctc.2021.100746. eCollection 2021 Jun.

Authors

S Gwynn Sturdevant¹, Susan S Huang², Richard Platt³, Ken Kleinman⁴

Affiliations

¹ Laboratory for Innovation Science at Harvard, 175 N. Harvard Street, Suite 1350, Boston, MA, 02134, USA.
² University of California, Irvine, 101 The City Drive South, City Tower, Suite 400, Mail Code: 4081, Orange, CA, 92868, USA.
³ Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, USA.
⁴ Department of Biostatistics and Epidemiology, 715 North Pleasant Street, University of Massachusetts, Amherst, MA, 01003, USA.

Abstract

In group or cluster-randomized trials (GRTs), matching is a technique that can be used to improve covariate balance. When baseline data are available, we suggest a strategy that can be used to achieve the desired balance between treatment and control groups across numerous potential confounding variables. This strategy minimizes the overall within-pair Mahalanobis distance; and involves iteratively: 1) making pairs that minimize the distance between pairs of clusters with respect to potentially confounding variables; 2) visually assessing the potential effects of these pairs and resulting possible randomizations; and 3) reweighting variables of selecting weights to make pairs of clusters. In step 2, we plot the between-arm differences with a parallel-coordinates plot. Investigators can compare plots of different weighting schemes to determine the one that best suits their needs prior to the actual, final, randomization. We demonstrate application of the approach with the Mupirocin-Iodophor Swap Out trial. A webapp is provided.

Keywords: Baseline covariates; Matching; Randomization; Randomized trials.