Browse by author
Lookup NU author(s): Dr Deborah Stocken
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Pancreatic cancer is the fifth most common cause of cancer death. Identification of defined patient groups based on a prognostic index may improve the prediction of survival and selection of therapy. Many prognostic factors have been identified often based on retrospective, underpowered studies with unclear analyses. Data from 653 patients were analysed. Continuous variables are often simplified assuming a linear relationship with log hazard or introducing a step function (dichotomising). Misspecification may lead to inappropriate conclusions but has not been previously investigated in pancreatic cancer studies. Models based on standard assumptions were compared with a novel approach using nonlinear fractional polynomial (FP) transformations. The model based on FP-transformed covariates was most appropriate and confirmed five previously reported prognostic factors: albumin, CA19-9, alkaline phosphatase, LDH and metastases, and identified three additional factors not previously reported: WBC, AST and BUN. The effects of CA19-9, alkaline phosphatase, AST and BUN may go unrecognised due to simplistic assumptions made in statistical modelling. We advocate a multivariable approach that uses information contained within continuous variables appropriately. The functional form of the relationship between continuous covariates and survival should always be assessed. Our model should aid individual patient risk stratification and the design and analysis of future trials in pancreatic cancer.
Author(s): Stocken DD, Hassan AB, Altman DG, Billingham LJ, Bramhall SR, Johnson PJ, Freemantle N
Publication type: Article
Publication status: Published
Journal: British Journal of Cancer
Year: 2008
Volume: 99
Issue: 6
Pages: 883-893
ISSN (print): 0007-0920
ISSN (electronic): 1532-1827
Publisher: Nature Publishing Group
URL: http://dx.doi.org/10.1038/sj.bjc.6604568
DOI: 10.1038/sj.bjc.6604568
Altmetrics provided by Altmetric