Essential Medical Statistics is a classic amongst medical statisticians. An introductory textbook, it presents statistics with a clarity and logic that demystifies the subject, while providing a comprehensive coverage of advanced as well as basic methods.
The second edition of Essential Medical Statistics has been comprehensively revised and updated to include modern statistical methods and modern approaches to statistical analysis, while retaining the approachable and non-mathematical style of the first edition. The book now includes full coverage of the most commonly used regression models, multiple linear regression, logistic regression, Poisson regression and Cox regression, as well as a chapter on general issues in regression modelling. In addition, new chapters introduce more advanced topics such as meta-analysis, likelihood, bootstrapping and robust standard errors, and analysis of clustered data.
Aimed at students of medical statistics, medical researchers, public health practitioners and practising clinicians using statistics in their daily work, the book is designed as both a teaching and a reference text. The format of the book is clear with highlighted formulae and worked examples, so that all concepts are presented in a simple, practical and easy-to-understand way. The second edition enhances the emphasis on choice of appropriate methods with new chapters on strategies for analysis and measures of association and impact.
Part A. Basics
1. Using this book
2. Defining the data
3. Displaying the data.
Part B. Analysis of numerical outcomes
4. Means, Standard Deviations and Standard Errors
5. The Normal Distribution
6. Confidence Interval for a Mean
7. Comparison of two means: confidence intervals, hypothesis tests and P-values
8. Using P-values and confidence intervals to interpret the results of statistical analyses
9. Comparison of means from several groups: analysis of variance
10. Linear Regression and Correlation
11. Multiple Regression
12. Goodness of fit and regression diagnostics
Part C. Analysis of binary outcomes
14. Probability, risks and odds (of disease)
15. Proportions and the binomial distribution
16. Comparing two proportions
17. Chi-squared tests for 2 x 2 and larger contingency tables
18. Controlling for confounding: stratification
19. Logistic regression: comparing two or more exposure groups
20. Logisitic regression: controlling for confounding and other extensions
21. Matched studies.
Part D. Longitudinal studies: Analysis of rates and survival times
22. Longitudinal studies, rates and the Poisson distribution
23. Comparing rates
24. Poisson regression
26. Survival analysis: displaying and comparing survival patterns
27. Regression analysis of survival data.
Part E. Statistical modelling
29. Regression modelling
30. Relaxing model assumptions
31. Analysis of clustered data
32. Systematic reviews and meta-analysis
33. Bayesian statistics.
Part F. Study design, analysis and interpretation
34. Linking analysis to study design: summary of methods
35. Calculation of Required Sample Size
36. Measurement error: assessment and implications
37. Measures of association and impact
38. Strategies for analysis
APPENDIX: Statistical Tables