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

Authors

  • Robert A. Greevy Jr.,

    Corresponding author
    1. VA Tennessee Valley Geriatric Research Education Clinical Center (GRECC), Nashville, TN, USA
    2. Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
    • Robert A. Greevy, Jr., PhD, Vanderbilt University School of Medicine, Department of Biostatistics, Nashville, TN 37232-2158, U.S.A. E-mail: robert.greevy@vanderbilt.edu

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  • Carlos G. Grijalva,

    1. Department of Preventive Medicine, Vanderbilt University, Nashville, TN, USA
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  • Christianne L. Roumie,

    1. VA Tennessee Valley Geriatric Research Education Clinical Center (GRECC), Nashville, TN, USA
    2. Department of Medicine, Vanderbilt University, Nashville, TN, USA
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  • Cole Beck,

    1. VA Tennessee Valley Geriatric Research Education Clinical Center (GRECC), Nashville, TN, USA
    2. Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
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  • Adriana M. Hung,

    1. VA Tennessee Valley Geriatric Research Education Clinical Center (GRECC), Nashville, TN, USA
    2. Department of Medicine, Vanderbilt University, Nashville, TN, USA
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  • Harvey J. Murff,

    1. VA Tennessee Valley Geriatric Research Education Clinical Center (GRECC), Nashville, TN, USA
    2. Department of Medicine, Vanderbilt University, Nashville, TN, USA
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  • Xulei Liu,

    1. VA Tennessee Valley Geriatric Research Education Clinical Center (GRECC), Nashville, TN, USA
    2. Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
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  • Marie R. Griffin

    1. VA Tennessee Valley Geriatric Research Education Clinical Center (GRECC), Nashville, TN, USA
    2. Department of Medicine, Vanderbilt University, Nashville, TN, USA
    3. Department of Preventive Medicine, Vanderbilt University, Nashville, TN, USA
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ABSTRACT

Purpose

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.

Methods

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.

Results

The RMD matching achieved better balance than simple randomization or MD randomization. In the first example, simple and MD randomization resulted in a 10% chance of seeing an absolute mean difference of greater than 26% in the percent of nonwhite patients per site; the RMD dramatically reduced that to 6%. The RMD achieved significant improvement over simple randomization even with as much as 20% of the data missing.

Conclusions

Reweighted Mahalanobis distance matching provides an easy-to-use tool that incorporates user knowledge and missing data. Copyright © 2012 John Wiley & Sons, Ltd.

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