Biostatistics: A Methodology for the Health Sciences, Second Edition

✍ Scribed by Gerald Van Belle, Lloyd D. Fisher, Patrick J. Heagerty, Thomas Lumley(auth.), Walter A. Shewhart, Samuel S. Wilks(eds.)

• Year: 2004
• Tongue: English
• Leaves: 890
• Series: Wiley Series in Probability and Statistics
• Category: Library

✦ Synopsis

A respected introduction to biostatistics, thoroughly updated and revised

The first edition of Biostatistics: A Methodology for the Health Sciences has served professionals and students alike as a leading resource for learning how to apply statistical methods to the biomedical sciences. This substantially revised Second Edition brings the book into the twenty-first century for today’s aspiring and practicing medical scientist.

This versatile reference provides a wide-ranging look at basic and advanced biostatistical concepts and methods in a format calibrated to individual interests and levels of proficiency. Written with an eye toward the use of computer applications, the book examines the design of medical studies, descriptive statistics, and introductory ideas of probability theory and statistical inference; explores more advanced statistical methods; and illustrates important current uses of biostatistics.

New to this edition are discussions of

• Longitudinal data analysis
• Randomized clinical trials
• Bayesian statistics
• GEE
• The bootstrap method

Enhanced by a companion Web site providing data sets, selected problems and solutions, and examples from such current topics as HIV/AIDS, this is a thoroughly current, comprehensive introduction to the field.Content:
Chapter 1 Introduction to Biostatistics (pages 1–9):
Chapter 2 Biostatistical Design of Medical Studies (pages 10–24):
Chapter 3 Descriptive Statistics (pages 25–60):
Chapter 4 Statistical Inference: Populations and Samples (pages 61–116):
Chapter 5 One? and Two?Sample Inference (pages 117–150):
Chapter 6 Counting Data (pages 151–207):
Chapter 7 Categorical Data: Contingency Tables (pages 208–252):
Chapter 8 Nonparametric, Distribution?Free, and Permutation Models: Robust Procedures (pages 253–290):
Chapter 9 Association and Prediction: Linear Models with One Predictor Variable (pages 291–356):
Chapter 10 Analysis of Variance (pages 357–427):
Chapter 11 Association and Prediction: Multiple Regression Analysis and Linear Models with Multiple Predictor Variables (pages 428–519):
Chapter 12 Multiple Comparisons (pages 520–549):
Chapter 13 Discrimination and Classification (pages 550–583):
Chapter 14 Principal Component Analysis and Factor Analysis (pages 584–639):
Chapter 15 Rates and Proportions (pages 640–660):
Chapter 16 Analysis of the Time to an Event: Survival Analysis (pages 661–708):
Chapter 17 Sample Sizes for Observational Studies (pages 709–727):
Chapter 18 Longitudinal Data Analysis (pages 728–765):
Chapter 19 Randomized Clinical Trials (pages 766–786):
Chapter 20 Personal Postscript (pages 787–816):