Data Analysis Plans: A Blueprint for Success Using SAS

"Finally, a book that describes how to formalize a data analysis plan without the strict formality of an industry-standard statistical analysis plan! Every analysis of any size should have a data analysis plan and Data Analysis Plans: A Blueprint for Success Using SAS tells you how. This book describes the overall rationale and structure of a data analysis plan with examples of what to include and the SAS code for many of the analyses.

"It will appeal to biostatisticians with the full range of experience: those engaged in their first major study analysis through those who have been conducting analyses for decades. The authors walk the reader through the contents and development of the plan, the conceptualization of the analysis, the initial analytic steps, and the generation of a report based on the data analysis plan.

"It is written in a "light" technical way so that a biostatistician new to SAS will have no problem understanding and implementing the code. The SAS code examples are easy to follow and understand, and the results are easy to interpret. While it will not take long to read and grasp the concepts, this is a book that you will want to keep handy for reference!"

Bruce A. Barton, Ph.D.
Research Professor
Director, Quantitative Methods Core
Department of Quantitative Health Sciences
University of Massachusetts Medical School

"Kathleen Jablonski and Mark Guagliardo's Data Analysis Plans: A Blueprint for Success Using SAS® is an excellent resource for all collaborative statisticians. Easy to read and follow, this book outlines the importance for pre-specified study design with corresponding hypotheses, data descriptions, analysis plans, and timelines. Very importantly, it discusses how to communicate progress and changes with other identified study team members, and I love the included example analysis plans with table shells of projected results. As a clinical trial biostatistician, I cannot wait to share this book with other colleagues to help improve and clarify processes for our projects!"

Emily V. Dressler
Associate Professor
University of Kentucky

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