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    A pattern-mixture model approach for handling missing continuous outcome data in longitudinal cluster randomized trials

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    Fiero_PMM_for_CRTs.pdf
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    Author
    Fiero, Mallorie H.
    Hsu, Chiu-Hsieh
    Bell, Melanie L.
    Affiliation
    Univ Arizona, Mel & Enid Zuckerman Coll Publ Hlth, Dept Epidemiol & Biostat
    Issue Date
    2017-11-20
    Keywords
    cluster randomized trials
    missing data
    multiple imputation
    pattern-mixture model
    
    Metadata
    Show full item record
    Publisher
    WILEY
    Citation
    Fiero MH, Hsu C‐H, Bell ML. A pattern‐mixture model approach for handling missing continuous outcome data in longitudinal cluster randomized trials. Statistics in Medicine. 2017;36:4094–4105. https://doi.org/10.1002/sim.7418
    Journal
    STATISTICS IN MEDICINE
    Rights
    Copyright © 2017 John Wiley & Sons, Ltd.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    We extend the pattern-mixture approach to handle missing continuous outcome data in longitudinal cluster randomized trials, which randomize groups of individuals to treatment arms, rather than the individuals themselves. Individuals who drop out at the same time point are grouped into the same dropout pattern. We approach extrapolation of the pattern-mixture model by applying multilevel multiple imputation, which imputes missing values while appropriately accounting for the hierarchical data structure found in cluster randomized trials. To assess parameters of interest under various missing data assumptions, imputed values are multiplied by a sensitivity parameter, k, which increases or decreases imputed values. Using simulated data, we show that estimates of parameters of interest can vary widely under differing missing data assumptions. We conduct a sensitivity analysis using real data from a cluster randomized trial by increasing k until the treatment effect inference changes. By performing a sensitivity analysis for missing data, researchers can assess whether certain missing data assumptions are reasonable for their cluster randomized trial.
    Note
    12 month embargo; published online: 07 August 2017
    ISSN
    0277-6715
    PubMed ID
    28783884
    DOI
    10.1002/sim.7418
    Version
    Final accepted manuscript
    Sponsors
    National Cancer Institute of the National Institutes of Health [P30 CA023074]
    Additional Links
    https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7418
    ae974a485f413a2113503eed53cd6c53
    10.1002/sim.7418
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