title: Incorporating temporal features of repeatedly measured covariates into tree-structured survival models.
abstract: Tree-structured survival methods empirically identify a series of covariate-based binary split points, resulting in an algorithm that can be used to classify new patients into risk groups and subsequently guide clinical treatment decisions. Traditionally, only fixed-time (e.g. baseline) values are used in tree-structured models. However, this manuscript considers the scenario where temporal features of a repeated measures polynomial model, such as the slope and/or curvature, are useful for distinguishing risk groups to predict future outcomes. Both fixed- and random-effects methods for estimating individual temporal features are discussed, and methods for including these features in a tree model and classifying new cases are proposed. A simulation study is performed to empirically compare the predictive accuracies of the proposed methods in a wide variety of model settings. For illustration, a tree-structured survival model incorporating the linear rate of change of depressive symptomatology during the first four weeks of treatment for late-life depression is used to identify subgroups of older adults who may benefit from an early change in treatment strategy.
authors.affiliation: Department of Psychiatry, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA. mel20@pitt.edu
authors.forename: Meredith L
authors.initials: ML
authors.lastname: Wallace
authors.forename: Stewart J
authors.initials: SJ
authors.lastname: Anderson
authors.forename: Sati
authors.initials: S
authors.lastname: Mazumdar
authors.forename: Lan
authors.initials: L
authors.lastname: Kong
authors.forename: Benoit H
authors.initials: BH
authors.lastname: Mulsant
mesh_terms: D000368:Aged; D000704:Analysis of Variance; D002986:Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D003866:Depressive Disorder / Q000188:drug therapy; D006801:Humans; D016014:Linear Models; D008875:Middle Aged; D015233:Models, Statistical; D016019:Survival Analysis; D013997:Time Factors