The purpose of this text is to enable biomedical researchers to use a number of advanced statistical methods that have proven valuable in medical research. The past forty years have seen an explosive growth in the development of biostatistics. As with so many aspects of our world, this growth has been strongly influenced by the development of inexpensive, powerful computers and the sophisticated software that has been written to run them. This has allowed the development of computationally intensive methods that can effectively model complex biomedical data sets. It has also made it easy to explore these data sets, to discover how variables are interrelated, and to select appropriate statistical models for analysis. Indeed, just as the microscope revealed new worlds to the eighteenth century, modern statistical software permits us to see interrelationships in large complex data sets that would have been missed in previous eras. Also, modern statistical software has made it vastly easier for investigators to perform their own statistical analyses. Although very sophisticated mathematics underlies modern statistics, it is not necessary to understand this mathematics to properly analyze your data with modern statistical software. What is necessary is to understand the assumptions required by each method, how to determine whether these assumptions are adequately met for your data, how to select the best model, and how to interpret the results of your analyses. The goal of this text is to allow investigators to effectively use some of the most valuable multivariate methods without requiring a prior understanding of more than high school algebra. Much mathematical detail is avoided by focusing on the use of a specific statistical software package.

This text grew out of my second semester course in biostatistics that I teach in our Master of Public Health program at the Vanderbilt University Medical School.