Using SAS for Data Management, Statistical Analysis, and Graphics
Quick and Easy Access to Key Elements of Documentation
Includes worked examples across a wide variety of applications, tasks, and graphics
A unique companion for statistical coders, Using SAS for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in SAS, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. Organized by short, clear descriptive entries, the book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods, and the creation of graphics.
Through the extensive indexing, cross-referencing, and worked examples in this text, users can directly find and implement the material they need. The text includes convenient indices organized by topic and SAS syntax. Demonstrating the SAS code in action and facilitating exploration, the authors present example analyses that employ a single data set from the HELP study. They also provide several case studies of more complex applications. Data sets and code are available for download on the book’s website.
Helping to improve your analytical skills, this book lucidly summarizes the features of SAS most often used by statistical analysts. New users of SAS will find the simple approach easy to understand while more expert SAS programmers will appreciate the invaluable source of task-oriented information.
Table of Contents
Introduction to SAS
Running SAS and a sample session
Learning SAS and getting help
Fundamental structures: Data step, procedures, and global statements
Work process: The cognitive style of SAS
Useful SAS background
Accessing and controlling SAS output: The Output Delivery System
The SAS Macro Facility: Writing functions and passing values
Interfaces: Code and menus, data exploration, and data analysis
Structure and meta-data
Derived variables and data manipulation
Merging, combining, and subsetting datasets
Date and time variables
Interactions with the operating system
Probability distributions and random number generation
Control flow, programming, and data generation
Common Statistical Procedures
Two sample tests for continuous variables
Linear Regression and ANOVA
Model comparison and selection
Tests, contrasts, and linear functions of parameters
Model parameters and results
Regression Generalizations and Multivariate Statistics
Generalized linear models
Models for correlated data
Further generalizations to regression models
Multivariate statistics and discriminant procedures
A compendium of useful plots
Options and parameters
Simulations and data generation
Power and sample size calculations
Sampling from a pathological distribution
Read variable format files and plot maps
Data scraping and visualization
Missing data: Multiple imputation
Appendix: The HELP Study Dataset
Ken Kleinman is an associate professor in the Department of Population Medicine at Harvard Medical School in Boston, Massachusetts. His research deals with clustered data analysis, surveillance, and epidemiological applications in projects ranging from vaccine and bioterrorism surveillance to observational epidemiology to individual-, practice-, and community-randomized interventions.
Nicholas J. Horton is an associate professor in the Department of Mathematics and Statistics at Smith College in Northampton, Massachusetts. His research interests include longitudinal regression models and missing data methods, with applications in psychiatric epidemiology and substance abuse research.
This book is a well-organized reference text that summarizes and illustrates SAS code and common SAS features most often used by statistical analysts and others engaged in research and data analysis. … a handy reference tool for common tasks performed in SAS due to the book’s task-oriented nature and the broad range of topics covered. This book would also nicely serve as a supplemental reference text for an introductory SAS programming class.
—Journal of Biopharmaceutical Statistics, Issue 3, 2011