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"This is a technical book on a technical subject but presented in a delightful way. There are many books on statistics for doctors but there are few that are excellent and this is certainly one of them. Statistics is not an easy subject to teach or write about. The authors have succeeded in producing a book that is as good as it can get. For the keen student who does not want a book for mathematicians, this is an excellent first book on medical statistics.""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. "Essential Medical Statistics" is supported by a web site. This useful online resource provides statistical datasets to download, as well as sample chapters and future updates.

Pt. A Basics.
1. Using this book.
2. Defining the data.
3. Displaying the data.
Pt. 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.
13. Transformations.
Pt. C Analysis of binary outcomes.
14. Probability, risk 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. Logistic regression: controlling for confounding and other extensions.
21. Matched studies.
Pt. D Longitudinal studies: Analysis of rates and survival times.
22. Longitudinal studies, rates and the Poisson distribution.
23. Comparing rates.
24. Poisson regression.
25. Standardization.
26. Survival analysis: displaying and comparing survival patterns.
27. Regression analysis of survival data.
Pt. E Statistical modelling.
28. Likelihood.
29. Regression modelling.
30. Relaxing model assumptions.
31. Analysis of clustered data.
32. Systematic reviews and meta-analysis.
33. Bayesian statistics.
Pt. 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.
App. Statistical tables.

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