1st Edition

Using SAS for Data Management, Statistical Analysis, and Graphics

By Ken Kleinman, Nicholas J. Horton Copyright 2010
    306 Pages 32 B/W Illustrations
    by CRC Press

    306 Pages
    by CRC Press

    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.

    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

    Data Management
    Structure and meta-data
    Derived variables and data manipulation
    Merging, combining, and subsetting datasets
    Date and time variables
    Interactions with the operating system
    Mathematical functions
    Matrix operations
    Probability distributions and random number generation
    Control flow, programming, and data generation
    Further resources
    HELP examples

    Common Statistical Procedures
    Summary statistics
    Bivariate statistics
    Contingency tables
    Two sample tests for continuous variables
    Further resources
    HELP examples

    Linear Regression and ANOVA
    Model fitting
    Model comparison and selection
    Tests, contrasts, and linear functions of parameters
    Model diagnostics
    Model parameters and results
    Further resources
    HELP examples

    Regression Generalizations and Multivariate Statistics
    Generalized linear models
    Models for correlated data
    Further generalizations to regression models
    Multivariate statistics and discriminant procedures
    Further resources
    HELP examples

    A compendium of useful plots
    Adding elements
    Options and parameters
    Saving graphs
    Further resources
    HELP examples

    Advanced Applications
    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
    Further resources

    Appendix: The HELP Study Dataset


    Subject Index
    SAS Index


    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