Statistical Methods for the Analysis of Biomedical Data

ISBN-13: 9780471394051 / Angielski / Twarda / 2002 / 712 str.

Robert F. Woolson; William R. Clarke; William R. Clarke

Opis

The new edition adds a chapter on multiple linear regression in biomedical research, with sections including the multiple linear regressions model and least squares; the ANOVA table, parameter estimates, and confidence intervals; partial f-tests; polynomial regression; and analysis of covariance.
* Organized by problem rather than method, so it guides readers to the correct technique for solving the problem at hand.

Recenzje

" useful in a course in biostatistics." ( Journal of Statistical Computation and Simulation, September 2005)

"...a nice overview of statistical topics...an excellent book to have...highly recommend this book for students and researchers..." (Statistical Methods in Medical Research, Vol 13, 2004)

" interesting and useful I recommend it as an addition to your statistical library, and if you already own the first edition, it would be worthwhile to update it." (The American Statistician, Vol. 58, No. 2, May 2004)

Spis treści

Preface to the 1987 Edition.

Preface to the 2002 Edition.

Acknowledgments.

1. Introduction.

2. Descriptive Statistics.

3. Basic Probability Concepts.

4. Further Aspects of Probability for Statistical Inference: Sampling, Probability Distributions, and Sampling Disctributions.

5. Confidence Intervals and Hypothesis Testing: General Considerations and Applications.

6. Comparison of Two Groups: t–Tests and Rank Tests.

7. Comparison of Two Groups: Chi–Square and Related Procedures.

8. Tests of Independence and Measure of Association for Two Random Variables.

9. Least–Square Regression Methods: Predicting One Variable from Another.

10. Comparing More Than Two Groups of Observations: Analysis of Variance for Comparing Groups.

11. Comparing More Than Two Groups of Observations: Rank Analysis of Variance for Group Comparisons.

12. Comparing More than Two Groups of Observations: Chi–Square and Related Procedures.

13. Special Topics in Analysis of Epidemiologic and Clinical Data: Studying Association Between a Disease and a Characteristic.

14. Estimation and Comparison of Survival Curves.

15. Multiple Linear Regression Methods: Predicting One Variable from Two or More Other Variables.

Appendix.

O autorze

ROBERT F. WOOLSON, PhD, is a Professor of Biostatistics and Associate Dean for Research at the University of Iowa College of Public Health.

WILLIAM R. CLARKE, PhD, is a Professor of Biostatistics at the University of Iowa College of Public Health.

Blurb

Praise for the First Edition

"This text is a very welcome addition to the field, both as a reference and classroom text."
–Publication of the International Statistical Institute

"This book is a perfectly organized source of reference for elementary statistical procedures used in clinical trials."
–Biomedical Journal

"I would recommend this book as an introductory biostatistics text for its clear presentation and worked examples, particularly in the chapters on the comparison of groups."
–Statistics in Medicine

Statistical Methods for the Analysis of Biomedical Data surveys a number of statistical topics commonly used in the analysis of biological and medical data, as well as several other topics such as observer agreement, standardization of rates, and methods for analysis of odds ratio. Applied in its approach, the book describes statistical procedures through the introduction of worked examples for each procedure.

The Second Edition includes a new chapter on multiple linear regression methods, new sections in each chapter on using SAS for computations, and revised and expanded problems. This authoritative volume also:
∗ Is organized by problem rather than method, guiding readers to the correct technique for solving the problem at hand
∗ Compares, in cases where more than one technique is applicable, the advantages and disadvantages of each option
∗ Develops, in detail, solutions to practical problems of design and analysis through worked examples
∗ Demonstrates how SAS output illustrates the results of analyses of true–to–life applications, such as predicting systolic blood pressure as a function of age, height, weight, and gender from data taken on Muscatine teenagers
∗ Includes methods for comparing two or more groups and evaluating the association between two variables, techniques for epidemiological analysis of 2x2 tables, and procedures for estimation and comparing survival curves

Statistical Methods for the Analysis of Biomedical Data makes an ideal reference for analysts and researchers in biostatistics, medicine, and other health–related fields, as well as a textbook for graduate courses in biostatistics.