Binary partitioning for continuous longitudinal data: categorizing a prognostic variable.

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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Abdolell M,LeBlanc M,Stephens D,Harrison RV

doi

10.1002/sim.1266

subject

Has Abstract

pub_date

2002-11-30 00:00:00

pages

3395-409

issue

22

eissn

0277-6715

issn

1097-0258

journal_volume

21

pub_type

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