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. FIRST. and LAST. Temporary Variables......Page 6 Summarizing Data Using PROC MEANS and PROC FREQ......Page 7 Restructuring SAS Data Sets Using Arrays......Page 8 Study One: Operations on a Clinical Database......Page 9 Study Three: Producing Summary Reports on a Library Data Set......Page 10 List of Data Files and SAS Data Sets......Page 11 FIRST. and LAST. Temporary Variables......Page 12 Summarizing Data Using PROC MEANS and PROC FREQ......Page 13 Restructuring SAS Data Sets Using Arrays......Page 14 Restructuring SAS Data Sets Using PROC TRANSPOSE......Page 15 Study One: Operations on a Clinical Database......Page 16 Study Two: Operations on Daily Weather Data and Ozone Levels......Page 17 Useful Macros......Page 18 List of Data Files and SAS Data Sets......Page 19 Demonstrating a DATA Step with and without a RETAIN Statement......Page 24 Generating Sequential SUBJECT Numbers Using a Retained Variable......Page 30 Using a SUM Statement to Create SUBJECT Numbers......Page 32 Demonstrating That Variables Read with a SET Statement Are Retained......Page 33 A Caution When Using a RETAIN Statement......Page 34 Using the LAG Function to Compute Differences......Page 36 Demonstrating Some Related Functions: LAG2, LAG3, and So Forth......Page 39 Demonstrating the DIF Function......Page 40 How to Create FIRST. and LAST. Temporary Variables......Page 42 Using More Than One BY Variable......Page 45 A Simple Application Using FIRST. and LAST. Variables......Page 47 Using a Flag Variable to Determine If a Particular Event Ever Occurred in Any One of Several Observations for Each Subject......Page 50 Counting the Number of Positive Outcomes for Each Patient......Page 52 Introduction......Page 56 Using PROC MEANS to Output Means to a Data Set......Page 57 Comparing CLASS and BY Statements with PROC MEANS......Page 60 Computing Other Descriptive Statistics......Page 61 Automatically Naming the Variables in the Output Data Set......Page 63 Demonstrating an Alternative Way to Select Specific Descriptive Statistics for Selected Variables......Page 64 Adding Additional Variables to the Summary Data Set Using an ID Statement......Page 65 Specifying More Than One CLASS Variable......Page 67 Selecting Multi-Way Breakdowns Using the TYPES Statement......Page 70 Using the PROC MEANS CHARTYPE Option to Simplify the _ TYPE_ Interpretation......Page 72 Comparing PROC MEANS and PROC FREQ for Creating an Output Data Set Containing Counts......Page 73 Counting Frequencies for a Two-Way Table......Page 75 Creating a Demonstration Data Set......Page 78 A Simple SQL Query......Page 80 Using PROC SQL to Count Observations within a BY Group......Page 81 Demonstrating a HAVING Clause......Page 82 Using PROC SQL to Create a Macro Variable......Page 83 Using a Summary Function to Compute Group Means......Page 85 Introduction......Page 88 Creating a New Data Set with Several Observations per Subject from a Data Set with One Observation per Subject......Page 89 Another Example of Creating Multiple Observations from a Single Observation......Page 92 Going from One Observation per Subject to Many Observations per Subject Using Multidimensional Arrays......Page 95 Demonstrating the Use of a Multidimensional Array......Page 97 An Alternative Program......Page 100 Another Example of a Multidimensional Array......Page 101 Going from One Observation to Several Observations......Page 104 Another Example of Creating Multiple Observations from a Single Observation......Page 107 Going from One Observation per Subject to Many Observations per Subject......Page 109 Creating a Data Set with One Observation per Subject from a Data Set with Multiple Observations per Subject......Page 111 Description of the Clinical Data Set......Page 117 Selecting the First or Last Visit for Each Patient......Page 118 Computing Differences between the First and Last Visits......Page 120 Another Method of Computing Differences between the First and Last Visits......Page 122 Computing Differences between Every Visit......Page 123 Counting the Number of Visits for Each Patient (DATA Step Approach)......Page 124 Counting the Number of Visits for Each Patient ( PROC MEANS)......Page 126 Counting the Number of Visits for Each Patient ( PROC SQL)......Page 127 Visits (DATA Step Approach)......Page 128 Visits (PROC FREQ Approach)......Page 129 Selecting All Patients with Two Visits (Using SQL in One Step)......Page 130 Using PROC SQL to Create a Macro Variable......Page 131 Computing Summary Statistics for Each Patient (Using PROC MEANS)......Page 132 Computing Summary Statistics for Each Patient (Using PROC SQL)......Page 133 Adding a Value from the First Visit to Each Subsequent Visit......Page 134 Looking Ahead: Making a Decision about the Current Observation Based on Information in the Next Observation......Page 137 Using Flags to Ascertain Vitamin Use......Page 140 Using PROC FREQ to Ascertain Vitamin Use......Page 141 Counting the Number of Routine Visits for Each Patient......Page 142 The OZONE Data Set......Page 144 Computing Weekly Averages......Page 145 Using the MOD Function to Group Data Values......Page 148 Computing a Moving Average for a Single Variable......Page 150 Introduction......Page 154 Computing the Number of Books per Patron Visit and by Library......Page 155 Computing the Number of Patrons by Day of Week and Library......Page 158 Generating a Table of LC Categories by Age Group and Overall......Page 160 Listing All or Part of a Data Set......Page 164 Computing Differences between Successive Observations......Page 166 Computing Differences between the First and Last Observations per Subject......Page 168 Computing a Moving Average......Page 170 Computing Cell Means and Counts......Page 172 Counting the Number of Observations per Subject......Page 174 The TEST_SCORES Data Set......Page 176 The CLINICAL Data Set......Page 177 The CLIN_FIRST Data Set......Page 179
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
Ron Cody has over 20 years of experience using and teaching the SAS System. As author of The SAS Workbook and Solutions and coauthor of two other successful SAS programming books, SAS Programming by Examples and Applied Statistics and the SAS Programming Language, Fourth Edition, Ron brings expert writing and programming skills to his newest SAS book. He has been a prolific and popular contributor to national, regional, and local user group conferences. In his spare time, Ron is a professor and researcher at the Robert Wood Johnson Medical School.
Booknews
This book for beginning to intermediate SAS users details techniques for conducting operations between observations in a SAS data set. Early chapters introduce SAS programming techniques for longitudinal data, while later chapters present case studies and detailed descriptions of longitudinal data macros. Cody is a professor and researcher at the Robert Wood Johnson Medical School. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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's easy 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. Longitudinal Data and SAS details useful techniques for conducting operations between observations in a SAS data set. For quick reference, the book is conveniently organized to cover tools, including an introduction to powerful SAS programming techniques for longitudinal data; case studies, including a variety of illuminating examples that use Ron's techniques; and macros, including detailed descriptions of helpful longitudinal data macros. Beginning to intermediate SAS users will appreciate this book's informative, easy-to-comprehend style. And users who frequently process longitudinal data will learn to make the most of their analyses by following Ron's methodologies. This book is part of the SAS Press program.
