Statistical issues in translational cancer research.
The explosion of knowledge about the basic biological processes and the genetics of cancer has led to increasing optimism that this knowledge can be put to practical clinical use in the near future. Indeed, important examples of translational approaches can already be found in the areas of drug discovery and development, disease diagnosis and classification, selection of therapeutic regimens for individual patients, and designing clinical trials. These are important developments but, as with any new approach, there is a danger of unwarranted enthusiasm and premature clinical application of laboratory results based on insufficient evidence. To carry out the translation of knowledge into practice with maximal efficiency and effectiveness, it is essential to conduct studies with appropriate designs and analyses based on sound statistical principles. This article provides an overview of some of these principles applied to assay development, validation of predictive models, and the design of clinical trials for targeted therapies.
George
Stephen L
SL
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Cancer and Leukemia Group B, Statistical Center, 2424 Erwin Road, Durham, NC 27705, USA. stephen.george@duke.edu
D000465:Algorithms; D014408:Biomarkers, Tumor; D001699:Biometry / Q000379:methods; D002986:Clinical Trials as Topic; D006801:Humans; D008495:Medical Oncology / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis* / Q000628:therapy*; D012107:Research Design
D014408:Biomarkers, Tumor