A Biostatistics Toolbox for Data Analysis - Steve Selvin

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Résumé :
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.

Biographie:
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.

Sommaire:
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.