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Modern Inference Based on Health Related Markers: Biomarkers and Statistical Decision Making provides a compendium of biomarkers based methodologies for respective health related fields and health related marker-specific biostatistical techniques. The book introduces correct and efficient testing mechanisms, including procedures based on bootstrap and permutation methods with the aim of making these techniques assessable to practical researchers. In the biostatistical aspect, it describes how to correctly state testing problems, but it also includes novel results, which have appeared in current statistical publications. In addition, the book discusses also modern applied statistical developments that consider data-driven techniques, including empirical likelihood methods and other simple and efficient methods to derive statistical tools for use in health related studies.
This book provides a compendium of statistical approaches for decision making, ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments. The book covers the underpinnings of appropriate statistical inference in addition to new theoretical methods, open problems, and novel testing procedures. It also offers software routines for a majority of the methods based on R and SAS.
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