Incorporating temporal features of repeatedly measured covariates into tree-structured survival models

Meredith L. Wallace, Stewart J. Anderson, Sati Mazumdar, Lan Kong, Benoit H. Mulsant

Research output: Contribution to journalArticle

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)181-196
Number of pages16
JournalBiometrical Journal
Volume54
Issue number2
DOIs
StatePublished - Mar 1 2012

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Incorporating temporal features of repeatedly measured covariates into tree-structured survival models'. Together they form a unique fingerprint.

  • Cite this