Adaptive-Cox model averaging for right-censored data

Yu Mei Chang, Pao Sheng Shen, Chun Shu Chen

Research output: Contribution to journalArticlepeer-review


In medical studies, Cox proportional hazards model is a commonly used method to deal with the right-censored survival data accompanied by many explanatory covariates. In practice, the Akaike's information criterion (AIC) or the Bayesian information criterion (BIC) is usually used to select an appropriate subset of covariates. It is well known that neither the AIC criterion nor the BIC criterion dominates for all situations. In this paper, we propose an adaptive-Cox model averaging procedure to get a more robust hazard estimator. First, by applying AIC and BIC criteria to perturbed datasets, we obtain two model averaging (MA) estimated survival curves, called AIC-MA and BIC-MA. Then, based on Kullback–Leibler loss, a better estimate of survival curve between AIC-MA and BIC-MA is chosen, which results in an adaptive-Cox estimate of survival curve. Simulation results show the superiority of our approach and an application of the proposed method is also presented by analyzing the German Breast Cancer Study dataset.

Original languageEnglish
Pages (from-to)9364-9376
Number of pages13
JournalCommunications in Statistics - Theory and Methods
Issue number19
StatePublished - 2 Oct 2017


  • Kullback–Leibler loss
  • data perturbation
  • information criterion
  • stabilization
  • variable selection


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