Longitudinal Data and SAS: A Programmer's Guide

• Publisher:   SAS Publishing
• Number Of Pages:   150
• Publication Date:   2001-10-15
• ISBN-10 / ASIN:   1580259243
• ISBN-13 / EAN:   9781580259248

Working with longitudinal data introduces a unique set of challenges. Once you've mastered the art of performing calculations within a single observation of a data set, you're faced with the task of performing calculations or making comparisons between observations. It is simple to look backward in data sets, but how do you look forward and across observations? Ron Cody provides straightforward answers to these and other questions. This book details useful techniques for conducting operations between observations in a SAS data set. For quick reference, the book is conveniently organized to cover: tools--an introduction to powerful SAS programming techniques for longitudinal data; case studies--a variety of illuminating examples that use Ron's techniques; and macros--detailed descriptions of helpful longitudinal data macros. Beginning to intermediate SAS users will appreciate this book's informative, easy-to-comprehend style. And those users who frequently process longitudinal data will learn to make the most of their analyses by following Ron's methodologies.

I have to give a very positive review to this book. Cody writes very nice introductory applied statistics books that emphasize SAS applications. This has good illustrations of a very important type of data analysis that biostatisticians doing clinical research need to know. Also, because in the analysis of clinical trials the FDA prefers analysis to be done using SAS, applications in SAS are important to have. If some or even most of this material is covered in another text as one reviewer suggests that does not mean that the biostatistician might not prefer to select this text which concentrates solely on longitudinal data. Also in the pharmaceutical industry where many clinical trials are conducted on longitudinal data, SAS programmers who are not statisticians are employed and books like this one can be of great use to them in their careers. The correct use of PROC Mixed in the analysis of longitudinal data is tricky and mistakes are easy to make.

This book is clearly written, but it's mostly a subsection of another book by the same author, Applied Statistics and the SAS Programming Language. There are a couple of case studies that are not included in the other book, but I don't know if that will be worth the price. Overall, the coverage is superficial. Managing longitudinal data involves heavy time series cross sectional analysis, but the author almost ignored time series data management completely. SAS is pretty weak in time series analysis and this book did not help much either.

This book has helped me so much at work. I like the clear explanation of what each line in the programs. Good examples of each topic.

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