Statistical modelling for the prognostic classification of patients with pancreatic cancer for optimisation of treatment allocation

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http://eprints.org/relation/isVolatileVersionOf http://etheses.bham.ac.uk/id/document/3244 http://eprints.org/relation/ispreviewThumbnailVersionOf http://etheses.bham.ac.uk/id/document/3244 http://eprints.org/relation/isVersionOf http://etheses.bham.ac.uk/id/document/3244 archive 4031 disk0/00/00/13/03 2011-01-25 12:18:32 2019-01-17 15:30:28 2011-07-19 16:32:53 thesis show 0 Stocken Deborah Dawn Freemantle Nick Johnson Philip University of Birmingham Statistical modelling for the prognostic classification of patients with pancreatic cancer for optimisation of treatment allocation unpub RC0254 RA0421 10col_mede 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and have an impact on future trial design and analysis. 2010-12 2010-03 graduation eng author unchecked This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. public 180 University of Birmingham School of Health and Population Sciences d_ph FALSE cancer_research_uk 2010-12 graduation