Reweighted Mahalanobis distance matching for cluster-randomized trials with missing data.

Pharmacoepidemiology and Drug Safety
Robert A GreevyMarie R Griffin

Abstract

This paper introduces an improved tool for designing matched-pairs randomized trials. The tool allows the incorporation of clinical and other knowledge regarding the relative importance of variables used in matching and allows for multiple types of missing data. The method is illustrated in the context of a cluster-randomized trial. A Web application and an R package are introduced to implement the method and incorporate recent advances in the area. Reweighted Mahalanobis distance (RMD) matching incorporates user-specified weights and imputed values for missing data. Weight may be assigned to missingness indicators to match on missingness patterns. Three examples are presented, using real data from a cohort of 90 Veterans Health Administration sites that had at least 100 incident metformin users in 2007. Matching is utilized to balance seven factors aggregated at the site level. Covariate balance is assessed for 10,000 randomizations under each strategy: simple randomization, matched randomization using the Mahalanobis distance, and matched randomization using the RMD. The RMD matching achieved better balance than simple randomization or MD randomization. In the first example, simple and MD randomization resulted in a 10% chanc...Continue Reading

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References

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Dec 25, 2010·Pharmacoepidemiology and Drug Safety·Christianne L RoumieMarie R Griffin
Jan 1, 2011·The American Statistician·Bo LuCole Beck

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Citations

Apr 26, 2013·Journal of the National Cancer Institute·Richard M HoffmanDavid F Penson
May 11, 2012·Pharmacoepidemiology and Drug Safety·Sebastian SchneeweissScott R Smith
Nov 26, 2014·Statistics in Medicine·Laura B BalzerUNKNOWN SEARCH Consortium
Aug 14, 2018·Statistics in Medicine·Lauren R Samuels, Robert A Greevy
Jul 2, 2021·Contemporary Clinical Trials Communications·S Gwynn SturdevantKen Kleinman

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