Modeling of many skewed biomarkers and missing data
An Example in Breast Cancer
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Multivariable prognostic models are widely used in all areas of science, in particular in cancer research. New scientific methods make it possible cost-effective measurement of many new biomarkers on frozen tissue from biopsies. In addition, biomarkers usually exhibit skewed distributions and their analysis is hampered by missing data. These issues (i.e. number of variables, missing data, and skewed distribution) create difficulty for prognostic modelling methods. In this book, methods to tackle each of these problems are discussed. Alternative approaches are presented with emphasize on practi...
Multivariable prognostic models are widely used in all areas of science, in particular in cancer research. New scientific methods make it possible cost-effective measurement of many new biomarkers on frozen tissue from biopsies. In addition, biomarkers usually exhibit skewed distributions and their analysis is hampered by missing data. These issues (i.e. number of variables, missing data, and skewed distribution) create difficulty for prognostic modelling methods. In this book, methods to tackle each of these problems are discussed. Alternative approaches are presented with emphasize on practical issues, and examples from papers published are presented. Methods are applied using a breast cancer data set as an example. This book is aimed at postgraduate students studying biostatistics and epidemiology, as well as researchers working in the field of survival analysis. Mohammad Reza Baneshi is assistant professor of Biostatistics at Research Center for Modeling in Health, affiliatedto Kerman University of Medical Sciences.
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