Biostatistics for Practitioners: An Interpretative Guide for Medicine and Biology deals with several aspects of statistics that are indispensable for researchers and students across the biomedical sciences. The book features a step-by-step approach, focusing on standard statistical tests, as well as discussions of the most common errors. The book is based on the author’s 40+ years of teaching statistics to medical fellows and biomedical researchers across a wide range of fields. Discusses how to use the standard statistical tests in the biomedical field, as well as how to make statistical inferences (t test, ANOVA, regression etc.) Includes non-standards tests, including equivalence or non-inferiority testing, extreme value statistics, cross-over tests, and simple time series procedures such as the runs test and Cusums Introduces procedures such as multiple regression, Poisson regression, meta-analysis and resampling statistics, and provides references for further studies
This book is a valuable source for students and researchers looking to expand or refresh their understanding of statistics as it applies to the biomedical and research fields.
Rather than provide detailed mathematics for each of these methods, the book emphasizes what healthcare practitioners need to know to interpret and incorporate the latest biomedical research into their practices.
The principles and methods described in this book are basic and apply to all medical subspecialties, psychology and education.
The interested reader can consult one of many mathematical statistics textbooks, for example, Larsen and Marx (1) or Rice (2). In the sections that follow, some of the more frequently encountered sampling distributions are discussed.
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Medicine deals with treatments that work often but not always, so treatment success must be based on probability. Statistical methods lift medical research from the anecdotal to measured levels of...
This book, a revised new edition of the successful Essentials of Biostatistics has been written to provide such an understanding to those who have little or no statistical background and who need to keep abreast of new findings in this fast ...
Lawson A 2009Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Chapmand and Hall/CRC, New York. ... Lawson A, Browne W and Vidal Rodeiro C 2003 Disease Mapping with WinBUGS and MLwiN, v2.10.
... 134, 135, 138 software, 142–144 survival parameterization, 130 nested case–control approach, 6 network algorithms, 52 Newton–Raphson (NR) algorithm, 154 Neyman–Pearson framework, 316, 317 non-informative, 310, 315, 318, 319, 321, ...