title: 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.
authors.affiliation: Population Health Sciences Research Institute, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada. abdo@sickkids.ca
authors.forename: M
authors.initials: M
authors.lastname: Abdolell
authors.forename: M
authors.initials: M
authors.lastname: LeBlanc
authors.forename: D
authors.initials: D
authors.lastname: Stephens
authors.forename: R V
authors.initials: RV
authors.lastname: Harrison
mesh_terms: D000293:Adolescent; D000367:Age Factors; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D019929:Cochlear Implantation / Q000379:methods*; D003638:Deafness / Q000628:therapy*; D006309:Hearing; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D009716:Numerical Analysis, Computer-Assisted; D011379:Prognosis; D013060:Speech; D016896:Treatment Outcome