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.
STEPHEN J. WALTERS is Professor of Medical Statistics and Clinical Trials in the School of Health and Related Research (ScHARR) at the University of Sheffield, UK. Stephen is a prolific researcher and writer, including the popular textbooks How to Display Data and How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research. He is a National Institute for Health Research (NIHR) Senior Investigator, and has developed several courses on teaching medical statistics to medical and health science students, clinicians and allied health professionals.
MICHAEL J. CAMPBELL is Emeritus Professor of Medical Statistics in the School of Health and Related Research (ScHARR) at the University of Sheffield, UK. Mike is a leading researcher in medical statistics and clinical trials with a national and international reputation. A prolific writer, Mike has written many best-selling textbooks on medical statistics and clinical trials including: Statistics at Square One, Statistics at Square Two, Sample Size Tables for Clinical Studies, and How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research.
DAVID MACHIN is Emeritus Professor of Medical Statistics in the School of Health and Related Research (ScHARR) at the University of Sheffield, UK. He was Foundation Director of the National Medical Research Council, Clinical Trials and Epidemiology Research Unit, Singapore, and Head of the MRC Cancer Trials Office, Cambridge, UK. He has published more than 250 peer reviewed articles, and several books on a wide variety of topics in statistics and medicine. His earlier experience included posts at the Universities of Wales, Leeds, Stirling, Southampton and Sheffield, a period with the European Organisation for Research and Treatment of Cancer in Brussels, Belgium, and at the World Health Organization in Geneva, Switzerland.
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
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