Binary partitioning for continuous longitudinal data: Categorizing a prognostic variable

M. Abdolell; M. LeBlanc; D. Stephens; R. V. Harrison

doi:10.1002/sim.1266

Binary partitioning for continuous longitudinal data: Categorizing a prognostic variable

M. Abdolell, M. LeBlanc, D. Stephens, R. V. Harrison

Research output: Contribution to journal › Article › peer-review

59 Citations (Scopus)

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.

Original language	English
Pages (from-to)	3395-3409
Number of pages	15
Journal	Statistics in Medicine
Volume	21
Issue number	22
DOIs	https://doi.org/10.1002/sim.1266
Publication status	Published - Nov 30 2002
Externally published	Yes

ASJC Scopus Subject Areas

Epidemiology
Statistics and Probability

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10.1002/sim.1266

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Abdolell, M., LeBlanc, M., Stephens, D., & Harrison, R. V. (2002). Binary partitioning for continuous longitudinal data: Categorizing a prognostic variable. Statistics in Medicine, 21(22), 3395-3409. https://doi.org/10.1002/sim.1266

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Binary partitioning for continuous longitudinal data: Categorizing a prognostic variable

Abstract

ASJC Scopus Subject Areas

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