Sorafenib is the current standard of care for patients with advanced hepatocellular carcinoma (aHCC) and has been shown to improve survival by about 3 months compared to placebo. However, survival varies widely from under three months to over two years. The aim of this study was to build a statistical model that allows personalised survival prediction following sorafenib treatment.
We had access to 1130 patients undergoing sorafenib treatment for aHCC as part of the control arm for two phase III randomised clinical trials (RCTs). A multivariable model was built that predicts survival based on baseline clinical features. The statistical approach permits both group-level risk stratification and individual-level survival prediction at any given time point. The model was calibrated, and its discrimination assessed through Harrell’s c-index and Royston-Sauerbrei’s R2D.
The variables influencing overall survival were vascular invasion, age, ECOG score, AFP, albumin, creatinine, AST, extra-hepatic spread and aetiology. The model-predicted survival very similar to that observed. The Harrell’s c-indices for training and validation sets were 0.72 and 0.70, respectively indicating good prediction.
Our model (‘PROSASH’) predicts patient survival using baseline clinical features. However, it will require further validation in a routine clinical practice setting.
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We are grateful to Professor Patrick Royston for his advice on the statistical methodology applied in this paper and to Bristol Myers Squibb and Pfizer for providing the data. This study has been previously presented at The International Liver Congress, 53rd annual meeting of the European Association for the Study of the Liver (EASL), Paris, France, 2018.
Concept and design: P.J., S.B. Statistical analysis: S.B., R.F., M.G.F. Drafting of the manuscript: all.