Statistical Models in Epidemiologyby David Clayton, Michael Hills
The most important concept in statistics is the probability model. Only by fully understanding this model can one fully understand statistical analysis. Utilizing models in epidemiology, the authors of this self-contained account have chosen to emphasize the role of likelihood. This approach to statistics is both simple and intuitively satisfying. More complex
The most important concept in statistics is the probability model. Only by fully understanding this model can one fully understand statistical analysis. Utilizing models in epidemiology, the authors of this self-contained account have chosen to emphasize the role of likelihood. This approach to statistics is both simple and intuitively satisfying. More complex problems can be tackled by natural extensions of the simple methods. This exploration of the statistical basis of epidemiology has been written specifically for professionals and graduate students in epidemiology, clinical epidemiology, or biostatistics. The simple prerequisitebasic training in biologyassumes no previous knowledge and the mathematics is deliberately kept at a manageable level. Based on a highly successful course by two internationally known authors, this book explains the essentials of statistics for all epidemiologists.
Description: This book provides a statistical foundation for performing epidemiologic analyses, beginning with a basic explanation of probability and likelihood function in early chapters and expanding on these concepts in later chapters to illustrate their application in epidemiology.
Purpose: The authors' stated objective is to present a basis for understanding statistical models commonly used in epidemiology. This goal is certainly laudable, because epidemiology students often have difficulty incorporating their knowledge of statistical theory with the more applied concepts taught in epidemiology courses. However, if viewed alone, this book falls somewhat short of achieving this goal, because both relevant theoretical and applied concepts are excluded as a result of combining the two disciplines.
Audience: The authors certainly seem credible authorities on the subject matter; both have affiliations with respected institutions and together they have taught numerous courses in epidemiology. However, although the book is intended for masters degree students in epidemiology or biostatistics with no previous statistical knowledge, the ideas presented become increasingly complex throughout the book and would seem arduous for a student at a beginning level.
Features: This book contains many diagrams that are very useful in illustrating complicated ideas. The table of contents and index are complete and accurate. There are numerous short chapters that focus on one key concept, a style more effective than combining many intricate and often confusing ideas together into a single chapter. The ordering of topics, however, is better suited for a statistical than for an epidemiological mind set. Important concepts are not always introduced in a sequence consistent with planning and undertaking an epidemiologic analysis. For example, the concept of statistical interaction should always be considered when evaluating any stratified analysis, yet it is not presented until much later in the regression analysis section.
Assessment: This book is most useful for an intermediate course in epidemiology. This book has several shortcomings that could be overcome by using this book as a supplement to other epidemiology texts rather than as the sole text. Additionally, this book does not replace a solid background in statistical theory. It does, however, help to bridge the gap between statistics and epidemiology, and thus provides a unique perspective.
- Oxford University Press, USA
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- 6.10(w) x 9.10(h) x 0.90(d)
Meet the Author
David Clayton, Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research
Michael Hills, London School of Hygiene and Tropical Medicine
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