Biostatistics: The Bare Essentials - Tapa blanda

Sinopsis

This new edition of Biostatistics: The Bare Essentials continues the tradition of translating biostatistics in the health sciences literature with clarity and irreverence. Students and practitioners alike applaud Biostatistics as a practical guide that exposes them to every statistical test they are likely to encounter, with careful conceptual explanations and a minimum of algebra. What's New? The previous edition of Bare Essentials presented hierarchical linear modeling, which first appeared in psychology journals and has only recently been described in the medical literature. The 3rd edition also introduced a chapter on testing for equivalence and non-inferiority as well as a chapter with information for getting started with the computer statistics program SPSS. A very positive review of the 3rd edition of the book by Dr. Naomi Vaisrub appeared in JAMA which praised the book but recommended covering topics in epidemiology, so in the 4th edition the authors took her up on it. They've also included an entirely new chapter, called "Measures of Impact," in which they discuss measures of incidence and prevalence, risk, morbidity and fatality rates, and the number needed to treat. They also delve into the Poisson distribution for doing regressions on count data. Likewise, the reader will find new sections on robust estimators of the mean, the problems of multiple hypothesis testing, bootstrapping and resampling, as well as an expanded section on nonparametric stats. Free of calculations and jargon, Bare Essentials speaks so plainly that you won't need a technical dictionary. The focus is on the concepts, not the math. The objective is to enable you to determine whether the research results are applicable to your own patients. Throughout, you'll find highlights of areas in which researchers misuse or misinterpret statistical tests. The authors have labeled these "C.R.A.P. Detectors" (Convoluted Reasoning and Anti-Intellectual Pomposity), and they help you identify faulty methodology and misuse of statistics.

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