Medical Statistics at a Glance, 2/e > Genetics

Medical Statistics at a Glance provides a concise and accessible introduction and revision aid for undergraduate medical students and anyone wanting a straightforward introduction to this complex subject. Following the familiar, easy-to-use at a Glance format, each topic is presented as a double-page spread with key facts accompanied by clear, informative tables, formulae and graphs.
This new edition of Medical Statistics at a Glance:
Contains a second colour throughout to enhance the visual appeal, making the subject even easier to understand
Features worked examples on each topic, with emphasis on computer analysis of data rather than hand calculations
Includes new topics on Rates and Poisson regression, Generalised linear models, Explanatory variables in statistical models and Regression models for clustered data.
Has an accompanying website http://www.medstatsaag.com/containing supplementary material including multiple choice questions (MCQs) with annotated answers for self-assessment

Preface
Handling data
1. Types of data
2. Data entry
3. Error checking and outliers
4. Displaying data graphically
5. Describing data (1) - the ‘average’
6. Describing data (2) - the ‘spread’
7. Theoretical distributions (1) - the Normal distribution
8. Theoretical distributions (2) - other distributions
9. Transformations
Sampling and estimation
10. Sampling and sampling distributions
11. Confidence Intervals
Study design
12. Study design I
13. Study design II
14. Clinical trials
15. Cohort studies
16. Case-control studies
Hypothesis testing
17. Hypothesis testing
18. Errors in hypothesis testing
Basic techniques for analysing data
Numerical data
19. A single group
20. Two related groups
21. Two unrelated groups
22. More than two groups
Categorical data
23. A single proportion
24. Two proportions
25. More than two categories
Regression and correlation
26. Correlation
27. The theory of linear regression
28. Performing a linear regression analysis
29. Multiple linear regression analysis
30. Binary outcomes and logistic regression
31. Rates and Poisson regression
32. Generalized linear models
33. Explanatory variables in statistical models
34. Issues in statistical modelling
Important considerations
35. Checking assumptions
36. Sample size calculations
37. Presenting results
Additional topics
38. Diagnostic tools
39. Assessing agreement
40. Evidence-based medicine
41. Methods for clustered data
42. Regression methods clustered data
43. Systematic reviews and meta-analysis
44. Survival analysis
45. Bayesian methods
Appendices
A. Statistical tables
B. Altman’s nomogram for sample size calculations
C. Typical computer output
D. Glossary
Index

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