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Kullback-Based Selection Criteria for Accelerated Failure Time Models: A Comparative Study
Abstract
Survival analysis involves modeling time-to-event data using multiple models, such as the Accelerated Failure Time (AFT) model. Choosing the most appropriate model is crucial. Various model selection criteria are available in the literature. This study examines the performance of new model selection criteria based on Kullback’s Symmetric Divergence within the Parametric AFT model framework. Simulations and real data were used to compare the Kullback Information Criterion (KIC) and its corrected version (KICc) with traditional information criteria, such as the Akaike Information Criterion (AIC), corrected AIC (AICc), improved AIC (AICsur), and Bayesian Information Criterion. Results showed that KICc outperforms other criteria in selecting the true model order in small samples and maintains
competitive predictive accuracy across conditions. KIC and KICc outperform the AIC family criteria for moderate and large sample sizes. The real data shows that AIC and AICsur tend to select overfitted models, while KICc and AICc favor underfitted models.
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