Optimal Three-Group Splits Based on a Survival Outcome

John Crowley (), Alan Mitchell (), Pingping Qu (), Gareth Morgan () and Bart Barlogie ()
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John Crowley: Cancer Research and Biostatistics
Alan Mitchell: Allergan, USA Inc
Pingping Qu: Cancer Research and Biostatistics
Gareth Morgan: University of Arkansas for Medical Sciences, Myeloma Institute
Bart Barlogie: Mt Sinai School of Medicine

A chapter in Frontiers of Biostatistical Methods and Applications in Clinical Oncology, 2017, pp 231-242 from Springer

Abstract: Abstract In clinical research it is often desirable to discretize a continuous or ordered covariate. In this paper, we investigate the use of various running ordered logrank tests for finding optimal 3-group splits based on a survival outcome and a single covariate. We first present a successful application of using the modified ordered logrank test (MOL) to find three prognostic groups on a myeloma dataset. We then evaluate through simulations the performance of the running ordered logrank tests and a hierarchical method based on recursive partitioning in different scenarios: (1) when the true underlying distribution has three-groups, (2) when there is a linear relationship between covariate and outcome, and (3) when there is no association between covariate and outcome. We conclude that the MOL is the most robust among all versions of the running ordered logrank tests if the underlying distribution truly has three-groups, although further research could help define when the MOL is the statistic of choice more generally for finding optimal 3-group splits.

Keywords: Logrank test; Running ordered logrank tests; Modified ordered logrank test; Optimal three-group splits (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-10-0126-0_14

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DOI: 10.1007/978-981-10-0126-0_14

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