The 5th edition of this popular introduction to statistics for the medical and health sciences has undergone a significant revision, with several new chapters added and examples refreshed throughout the book. Yet it retains its central philosophy to explain medical statistics with as little technical detail as possible, making it accessible to a wide audience.
Helpful multi-choice exercises are included at the end of each chapter, with answers provided at the end of the book. Each analysis technique is carefully explained and the mathematics kept to minimum. Written in a style suitable for statisticians and clinicians alike, this edition features many real and original examples, taken from the authors' combined many years' experience of designing and analysing clinical trials and teaching statistics.
Students of the health sciences, such as medicine, nursing, dentistry, physiotherapy, occupational therapy, and radiography should find the book useful, with examples relevant to their disciplines. The aim of training courses in medical statistics pertinent to these areas is not to turn the students into medical statisticians but rather to help them interpret the published scientific literature and appreciate how to design studies and analyse data arising from their own projects. However, the reader who is about to design their own study and collect, analyse and report on their own data will benefit from a clearly written book on the subject which provides practical guidance to such issues.
The practical guidance provided by this book will be of use to professionals working in and/or managing clinical trials, in academic, public health, government and industry settings, particularly medical statisticians, clinicians, trial co-ordinators. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations.
Contents
Preface, xxx
1 Uses and Abuses of Medical Statistics
2 Displaying and summarising data
3. Summary measures for binary data
4 Probability and distributions
5 Populations, Samples, Standard Errors and Confidence Intervals
6 Hypothesis testing, P-values and statistical inference
7 Comparing two or more groups with continuously measured data
8 Comparing groups of binary and categorical data
9 Correlation and linear regression
10 Logistic Regression
11 Survival Analysis
12 Reliability and Method Comparison Studies
13 Evaluation of diagnostic tests
14 Observational Studies
15 The Randomised Controlled Trial
16 Sample Size Issues
17 Other statistical methods
18 Meta-analysis
19 Common Mistakes and Pitfalls
References, xxx
Solutions to exercises, xxx
Statistical Tables, xxx
Index, xxx
