Combines brief accounts of theory with interesting examples of medical data analyzed using SAS
Emphasizes the practical aspects of implementing each statistical technique and interpreting the SAS output
Includes all relevant SAS code and makes all of the code and data sets available for download
Statistical analysis is ubiquitous in modern medical research. Logistic regression, generalized linear models, random effects models, and Cox's regression all have become commonplace in the medical literature. But while statistical software such as SAS make routine application of these techniques possible, users who are not primarily statisticians must take care to correctly implement the various procedures and correctly interpret the output.
Statistical Analysis of Medical Data Using SAS demonstrates how to use SAS to analyze medical data. Each chapter addresses a particular analysis method. The authors briefly describe each procedure, but focus on its SAS implementation and properly interpreting the output. The carefully designed presentation relegates the theoretical details to "Displays," so that the code and results can be explored without interruption. All of the code and data sets used in the book are available for download from either the SAS Web site or www.crcpress.com.
Der and Everitt, authors of the best-selling Handbook of Statistical Analyses Using SAS, bring all of their considerable talent and experience to bear in this book. Step-by-step instructions, lucid explanations and clear examples combine to form an outstanding, self-contained guide--suitable for medical researchers and statisticians alike--to using SAS to analyze medical data.
Table of Contents
1. An Introduction to SAS 2. Describing and Summarizing Data 3. Basic Inference 4. Scatterplots Correlation- Simple Regression and Smoothing 5. Analysis of Variance and Covariance 6. Multiple Regression 7. Logistic Regression 8. The Generalized Linear Model 9. Generalized Additive Models 10. Nonlinear Regression Models 11. The Analysis of Longitudinal Data 1 12. The Analysis of Longitudinal Data 2- Models for Normal Response Variables 13. The Analysis of Longitudinal Data 3- Non-Normal Response 14. Survival Analysis 15. Analysis Multivariate Date- Principal Components and Cluster Analysis
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