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A Biostatistics Toolbox for Data Analysis

A Biostatistics Toolbox for Data Analysis

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Steve Selvin
Cambridge University Press, 10/20/2015
EAN 9781107113084, ISBN10: 1107113083

Hardcover, 578 pages, 26.1 x 18.4 x 3.5 cm
Language: English

This sophisticated package of statistical methods is for advanced master's (MPH) and PhD students in public health and epidemiology who are involved in the analysis of data. It makes the link from statistical theory to data analysis, focusing on the methods and data types most common in public health and related fields. Like most toolboxes, the statistical tools in this book are organized into sections with similar objectives. Unlike most toolboxes, however, these tools are accompanied by complete instructions, explanations, detailed examples, and advice on relevant issues and potential pitfalls - conveying skills, intuition, and experience. The only prerequisite is a first-year statistics course and familiarity with a computing package such as R, Stata, SPSS, or SAS. Though the book is not tied to a particular computing language, its figures and analyses were all created using R. Relevant R code, data sets, and links to public data sets are available from www.cambridge.org/9781107113084.

Part I. Basics
1. Statistical distribution
2. Confidence intervals
3. A weighted average
4. Two discrete probability functions
5. Correlation
Part II. Applications
6. The 2 x 2 table
7. Linear bivariate regression model
8. The 2 x k table
9. The log-linear Poisson regression model
10. Two-way and three-way tables analysis
11. Bootstrap analysis
12. Graphical analysis
13. The variance
14. The log-normal distribution
15. Nonparametric analysis
Part III. Survival
16. Rates
17. Nonparametric survival analysis
18. The Weibull survival function
Part IV. Epidemiology
19. Prediction, a natural measure of performance
20. The attributable risk summary
21. Time/space analysis
22. ROC curve and analysis
Part V. Genetics
23. Selection
a statistical description
24. Mendelian segregation analysis
25. Admixed populations
26. Nonrandom mating
Part VI. Theory
27. Statistical estimation
Part VII. R-Appendix.