title: Time-dependent tree-structured survival analysis with unbiased variable selection through permutation tests.
abstract: Incorporating time-dependent covariates into tree-structured survival analysis (TSSA) may result in more accurate prognostic models than if only baseline values are used. Available time-dependent TSSA methods exhaustively test every binary split on every covariate; however, this approach may result in selection bias toward covariates with more observed values. We present a method that uses unbiased significance levels from newly proposed permutation tests to select the time-dependent or baseline covariate with the strongest relationship with the survival outcome. The specific splitting value is identified using only the selected covariate. Simulation results show that the proposed time-dependent TSSA method produces tree models of equal or greater accuracy as compared to baseline TSSA models, even with high censoring rates and large within-subject variability in the time-dependent covariate. To illustrate, the proposed method is applied to data from a cohort of bipolar youths to identify subgroups at risk for self-injurious behavior.
authors.affiliation: Departments of Psychiatry and Statistics, University of Pittsburgh, Pittsburgh, PA, U.S.A.
authors.forename: M L
authors.initials: ML
authors.lastname: Wallace
mesh_terms: D000293:Adolescent; D000465:Algorithms; D015982:Bias; D001714:Bipolar Disorder / Q000523:psychology; D002965:Classification / Q000379:methods*; D015331:Cohort Studies; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D011594:Psychometrics / Q000379:methods*; D012307:Risk Factors; D016728:Self-Injurious Behavior / Q000523:psychology; D016019:Survival Analysis; D013997:Time Factors
keywords: bipolar disorder; permutation test; recursive partitioning; repeated measures; variable selection