Common Statistical Methods for Clinical Research with SAS(R) Examples

Common Statistical Methods for Clinical Research with SAS Examples, Second Edition, written by Glenn Walker, provides clinical researchers with an invaluable aid for understanding the statistical methods cited most frequently in clinical protocols, statistical analysis plans, clinical and statistical reports, and medical journals. The text is written in a way that takes the non-statistician through each test using examples, yet substantive details are presented that benefit even the most experienced data analyst. Introductory chapters provide elementary statistical concepts as applied to clinical trials and an overview of statistical inference, including discussions about power, calculating sample size, p-values, and the logic behind hypothesis testing. The statistical methods discussed here represent a composite of methods most commonly used in the analysis of clinical research data. Numerous examples taken from clinical research show how to calculate statistical data both manually and using SAS. This revised edition includes sections about crossover designs and multiple comparison methods, a discussion of interim analyses, 50% more examples, and reader exercises. Extensions of methods previously presented only in their most elementary form are shown in new examples. Glenn Walker and Jack Shostak's Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, is a thoroughly updated edition of the popular introductory statistics book for clinical researchers. This new edition has been extensively updated to include the use of ODS graphics in numerous examples as well as a new emphasis on PROC MIXED. Straightforward and easy to use as either a text or a reference, the book is full of practical examples from clinical research to illustrate both statistical and SAS methodology. Each example is worked out completely, step by step, from the raw data. Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, is an applications book with minimal theory. Each section begins with an overview helpful to nonstatisticians and then drills down into details that will be valuable to statistical analysts and programmers. Further details, as well as bonus information and a guide to further reading, are presented in the extensive appendices. This text is a one-source guide for statisticians that documents the use of the tests used most often in clinical research, with assumptions, details, and some tricks--all in one place.