Modelling survival in hepatocellular carcinoma

Objectives:

To identify the pattern of the risk of death over long-term in unresectable hepatocellular carcinoma by determining the appropriate distribution to extrapolate overall survival and to assess the role of the Weibull distribution as the standard survival model in oncology.

Research design and methods:

To select the appropriate distribution, three types of data sources have been analysed. Patient level data from two randomized controlled trials and published Kaplan-Meier curves from a systematic literature review provided short term follow-up data. They were supplemented with patient level data, with long-term follow-up from the Cancer Institute New South Wales, Australia. Published Kaplan-Meier curves were read in and a time-to-event dataset was created. Distributions were fitted to the data from the different sources separately. Their fit was assessed visually and compared using statistical criteria based on log-likelihood, the Akaike information criterion (AIC), and the Bayesian information criterion (BIC).

Results:

Based on both published and patient-level, and both short- and long-term follow-up data, the Weibull distribution, used very often in cost-effectiveness models in oncology, does not seem to offer a good fit in hepatocellular carcinoma among the different survival models. The best fitting distribution appears to be the lognormal, with loglogistic as the second-best fitting function. Results were consistent between the different sources of data.

Conclusions:

In unresectable hepatocellular carcinoma, the Weibull model, which is often treated at the gold standard, does not appear to be appropriate based on different sources of data (two clinical trials, a retrospective database and published Kaplan-Meier curves). Lognormal distribution seems to be the most appropriate distribution for extrapolating overall survival.