Time-dependent tree-structured survival analysis with unbiased variable selection through permutation tests

• Published in: Statistics in medicine Vol. 33; no. 27; pp. 4790 - 4804
• Main Author: Wallace, M. L.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 30.11.2014; Wiley Subscription Services, Inc
• Subjects: Adolescent; Algorithms; Bias; bipolar disorder; Bipolar Disorder - psychology; Classification - methods; Cohort Studies; Computer Simulation; Humans; Linear Models; Medical statistics; permutation test; Psychometrics - methods; recursive partitioning; repeated measures; Risk Factors; Self-Injurious Behavior - psychology; Simulation; Survival Analysis; Time Factors; variable selection; Variables; permutation test; bipolar disorder; variable selection; recursive partitioning; repeated measures
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.6261

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. Copyright © 2014 John Wiley & Sons, Ltd.