Parametric survival models for predicting the benefit of adjuvant chemoradiotherapy in gallbladder cancer

The Cox proportional hazards model is the most commonly used survival model in oncology; however, this semi-parametric model may not be the most appropriate survival model when the proportionality assumption does not hold. In this study, we consider the use of several types of accelerated failure time parametric survival techniques for modeling the benefit of adjuvant chemoradiotherapy for gallbladder cancer. In comparing the Weibull, exponential, log-logistic, and log-normal models, we found that the log-normal had the most favorable Akaike Information Criterion, and additional analyses of this model indicated that our gallbladder cancer dataset exhibited a good fit with the log-normal cumulative hazard function. This log-normal survival model can be used to help predict which patients will benefit from adjuvant chemoradiotherapy.