A Biostatistics Toolbox for Data Analysis

This book delivers a sophisticated package of statistical methods for advanced master's (MPH) and PhD students in public health and epidemiology, involved in data analysis.

Steve Selvin (Author)

9781107113084, Cambridge University Press

Hardback, published 20 October 2015

578 pages, 146 b/w illus. 290 tables
26.1 x 18.4 x 3.5 cm, 1.2 kg

'Professor Selvin is a master at making statistical procedures and their complex underpinnings accessible to students of all levels of expertise. This book is a brilliant compendium of Professor Selvin's tremendous understanding of the breadth and depth of biostatistical tools that he delivers to the reader with superb clarity. A broad range of salient statistical concepts are covered, pleasantly anchored with a brief history, described formally for the more initiated reader, and expertly illustrated with real-life data examples that are readily understood by the less mathematically inclined. Researchers from a myriad of scientific disciplines seeking masterful guidance about conducting their statistical data analysis will absolutely want this book at their fingertips.' Gary Shaw, Stanford University, California

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.

Subject Areas: Biology, life sciences [PS], Probability & statistics [PBT], Epidemiology & medical statistics [MBNS]