Oxford Handbook of Medical Statistics

Oxford Medical Handbooks

by Janet L. Peacock

To practice evidence-based medicine, doctors need to understand how research is conducted and be able to critically appraise research evidence. A sound understanding of medical statistics is essential for the correct evaluation of medical research and the appropriate implementation of findings in clinical practice.Written in an easily accessible style, the Oxford Handbook of Medical Statistics provides doctors and medical students with a concise and thorough account of this often difficult subject. It promotes understanding and interpretation of statistical methods across a wide range of topics, from study design and sample size considerations, through t- and chi-squared tests, to complex multifactorial analyses, using examples from published research. References for further reading are givenfor more information on specific topics.Helping readers to conduct their own research or critically appraise other's work, this volume provides all the information readers need to understand and interpret medical statistics.

Genres: Medical, Medicine, Nonfiction

540 pages, Kindle Edition

First published January 1, 2010

Reviews

[Usfromdk · Rating 2 out of 5]

The coverage is excessively focused on significance testing. The book also provides very poor coverage of model selection topics, where the authors not once but repeatedly recommend employing statistically invalid approaches to model selection (the authors recommend using hypothesis testing mechanisms to guide model selection, as well as using adjusted R-squared for model selection decisions - both of which are frankly awful ideas, for reasons which are obvious to people familiar with the field of model selection. "Generally, hypothesis testing is a very poor basis for model selection […] There is no statistical theory that supports the notion that hypothesis testing with a fixed α level is a basis for model selection." "While adjusted R2 is useful as a descriptive statistic, it is not useful in model selection" - quotes taken directly from Burnham & Anderson Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach).

The authors do not at any point in the coverage even mention the option of using statistical information criteria to guide model selection decisions, and frankly repeatedly recommend doing things which are known to be deeply problematic. The authors also cover material from Borenstein and Hedges' meta-analysis text in the book, yet still somehow manage to give poor advice in the context of meta-analysis along similar lines (implicitly advising people to base model decisions within the context of whether to use fixed effects or random effects on the results of heterogeneity tests, despite this approach being criticized as problematic in the formerly mentioned text).

Basic and not terrible, but there are quite a few problems with this text.

[Mrwa Hamdan · Rating 5 out of 5]

Oxford handbooks series are one of the best medical references.
With this amazing one talking about statistics, giving you the necessary theoretical background on researches, data handling as well as statistical methods for your analysis helping you to grow your knowledge further in the field of bio-statistics which is going to become my work field in the next few months.
between terms like P value, CI, chi-squard test, you will go deeper inside the understanding of methodology & thesis behind medical guidelines & recommendations.
learning medical statistics means being one of the Giants among your colleagues.

[Kei · Rating 5 out of 5]

This book unfortunately didn't exist when I was still in medical school. Definitely a must-read textbook for medical students, junior physicians, or medical doctors.