Written in a simple tone and chapters are organized in logical order to ease the process of understanding. Stresses on the importance and appropriateness of statistical methods, their assumptions, validity and interpretation in the context of clinical data.
Each chapter is organized into Learning Objectives, Introduction of various statistical methods illustrated with Worked Examples and graphical methods as appropriate, ending with summarized Key Points.
Review Questions, Exercises and Multiple Choice Questions enable the reader a quick grasp of and greater insight into the methods presented in the text. It covers the theoretical underpinnings essential to understanding subsequent core methodologies in the field.
Drawing on his extensive experience teaching graduate-level biostatistics courses and working in the pharmaceutical industry, the author explains the main principles of statistical inference with many examples and exercises. Extended examples illustrate key concepts in depth using a specific biostatistical context. In addition, the author uses simulation to reinforce the repeated sampling interpretation of numerous statistical concepts.
Reducing the computational complexities, he provides simple R functions for conducting simulation studies. This text gives graduate students with diverse backgrounds across the health, medical, social, and mathematical sciences a solid, unified foundation in the principles of statistical inference.
This groundwork will lead students to develop a thorough understanding of biostatistical methodology. The first third of the book presents an integrated overview and introduction to experimental design and statistical inference. The rest of the book provides an extensively cross-referenced set of brief critiques of sample case studies embodying all the most common statistical errors or design problems found in the biological literature.
These specific cases are effective for teaching the principles of biostatistics. The examples are drawn primarily from biomedicine, yet the book is also valuable to psychologists, social scientists, environmental scientists, ecologists, and any discipline employing statistics.
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