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Time‐dependent tree‐structured survival analysis with unbiased variable selection through permutation tests
Author(s) -
Wallace M. L.
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6261
Subject(s) - covariate , statistics , censoring (clinical trials) , survival analysis , selection (genetic algorithm) , computer science , baseline (sea) , permutation (music) , accelerated failure time model , mathematics , biology , machine learning , physics , fishery , acoustics
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.