A Biostatistics Toolbox for Data Analysis

About this book

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 A Biostatistics Toolbox for Data Analysis 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 A Biostatistics Toolbox for Data Analysis 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 online.

Contents

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

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Biography

Steve Selvin is a Professor of Biostatistics in the School of Public Health at University of California, Berkeley and was the head of the division from 1977 to 2004. He has published over 250 papers and authored several textbooks in the fields of biostatistics and epidemiology. His book, Survival Analysis for Epidemiologic and Medical Research, was published by Cambridge University Press in 2008.