Binary partitioning for continuous longitudinal data: categorizing a prognostic variable

Authors

  • M. Abdolell,

    Corresponding author
    1. Population Health Sciences Research Institute, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada
    2. Department of Public Health Sciences, University of Toronto, Toronto, ON, M5S 1A8, Canada
    • Population Health Sciences Research Institute, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada
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  • M. LeBlanc,

    1. Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N., MP-557, Seattle, WA 98109, U.S.A.
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  • D. Stephens,

    1. Population Health Sciences Research Institute, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada
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  • R. V. Harrison

    1. Department of Otolaryngology, Brain and Behaviour Division, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada
    2. Department of Otolaryngology, University of Toronto, Toronto, ON, M5S 1A8, Canada
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Abstract

We investigate a binary partitioning algorithm in the case of a continuous repeated measures outcome. The procedure is based on the use of the likelihood ratio statistic to evaluate the performance of individual splits. The procedure partitions a set of longitudinal data into two mutually exclusive groups based on an optimal split of a continuous prognostic variable. A permutation test is used to assess the level of significance associated with the optimal split, and a bootstrap confidence interval is obtained for the optimal split. Copyright © 2002 John Wiley & Sons, Ltd.

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