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Statistical Testing Strategies in the Health Sciences 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
is organized primarily based on the type of questions to be
answered by inference procedures or according to the general type
of mathematical derivation. It establishes the theoretical
framework for each method, with a substantial amount of chapter
notes included for additional reference. It then focuses on the
practical application for each concept, providing real-world
examples that can be easily implemented using corresponding
statistical software code in R and SAS. The book also explains the
basic elements and methods for constructing correct and powerful
statistical decision-making processes to be adapted for complex
statistical applications. With techniques spanning robust
statistical methods to more computationally intensive approaches,
this book shows how to apply correct and efficient testing
mechanisms to various problems encountered in medical and
epidemiological studies, including clinical trials. Theoretical
statisticians, medical researchers, and other practitioners in
epidemiology and clinical research will appreciate the book's novel
theoretical and applied results. The book is also suitable for
graduate students in biostatistics, epidemiology, health-related
sciences, and areas pertaining to formal decision-making
mechanisms.
Statistical Testing Strategies in the Health Sciences 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
is organized primarily based on the type of questions to be
answered by inference procedures or according to the general type
of mathematical derivation. It establishes the theoretical
framework for each method, with a substantial amount of chapter
notes included for additional reference. It then focuses on the
practical application for each concept, providing real-world
examples that can be easily implemented using corresponding
statistical software code in R and SAS. The book also explains the
basic elements and methods for constructing correct and powerful
statistical decision-making processes to be adapted for complex
statistical applications. With techniques spanning robust
statistical methods to more computationally intensive approaches,
this book shows how to apply correct and efficient testing
mechanisms to various problems encountered in medical and
epidemiological studies, including clinical trials. Theoretical
statisticians, medical researchers, and other practitioners in
epidemiology and clinical research will appreciate the book's novel
theoretical and applied results. The book is also suitable for
graduate students in biostatistics, epidemiology, health-related
sciences, and areas pertaining to formal decision-making
mechanisms.
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