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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
An Introduction to SAS
Describing and Summarizing Data
Basic Inference
Scatterplots Correlation: Simple Regression and Smoothing
Analysis of Variance and Covariance
Multiple Regression
Logistic Regression
The Generalized Linear Model
Generalized Additive Models
Nonlinear Regression Models
The Analysis of Longitudinal Data I
The Analysis of Longitudinal Data II: Models for Normal Response Variables
The Analysis of Longitudinal Data III: Non-Normal Response
Survival Analysis
Analysis Multivariate Date: Principal Components and Cluster Analysis
References
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