Pancreatic cancer is a common cause of cancer death and is difficult to diagnose and treat. A prognostic index can be used in clinical practice to predict survival. Thirty six prognostic factor studies were identified but size and statistical methods were inappropriate. Continuous variables are often simplified incorrectly i) assuming linear relationships between predictors and log-hazard or ii) using dichotomisation. Non-linearity is addressed for the first time in this disease site using restricted cubic spline and fractional polynomial functions. Multivariable models containing non-linear transformations gave a substantially better fit. Important effects of some covariates were unrecognised under simplistic assumptions. The fitted functions generated by the two methods were similar. A direct comparison of these strategies was based on assessing the difference in the AIC values by calculating a sampling distribution in multiple bootstrap resamples. Model validation is also addressed for the first time in this disease and suggested minimal over-fitting with reproducible prognostic information when fitted to external data. This thesis provides the first validated prognostic tool in advanced pancreatic cancer developed using appropriate statistical methodology. Risk-sets identified by the model could help clinicians target treatments to patients more appropriately and have an impact on future trial design and analysis.
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Statistical modelling for the prognostic classification of patients with pancreatic cancer for optimisation of treatment allocation
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