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Published on: October 23, 2020
Binary partitioning for continuous longitudinal data: categorizing a prognostic variable
M Abdolell1, M LeBlanc, D Stephens
1Population Health Sciences Research Institute, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada. abdo@sickkids.ca
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.
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