the reading and interpretation of clinical research articles that use increasingly complex statistical techniques. It addresses clinically relevant topics in biostatistics beyond the usual introduction to linear models, such as survival analysis and evaluation of screening tests. The text emphasizes the importance of understanding the underlying logic of statistical inference and statistical models to support correct interpretation and effective translation into practice. It promotes appropriate statistical method selection for conducting translational research. With a focus on disseminating information in easily understandable language, the text addresses basic statistical reasoning and four different classes of statistical models. The appendix provides refreshers on the algebraic underpinnings of statistics. More complex algebraic derivations are highlighted in boxes throughout the text chapters. The text explains how to work with the exponential expressions and logarithms necessary for the interpretation of logistic and hazard regression models and features clear explanations of more sophisticated statistical models, inference, and analyses. Chapters include examples from current research and multiple exercises designed to reinforce learning. Key Features: End-of-chapter exercises include both problems of interpretation and numerical problems that can be solved via hand calculations. For instructors and students interested in practical data analysis, data sets and practice problems are available from Springer Publishing Company's website with instructions in SPSS, STATA, and SAS formats. At the end of each chapter is a "Literature Application" text box with interpretation questions about a recent research article that highlights the statistical model discussed in the chapter. Throughout the book, text boxes highlight the most important algebraic formulas useful in interpreting statistical methods. A chapter on data management practices and ethical issues of privacy maintenance is included. Nine appendices provide tables of major probability distributions, for example, normal, t- and F-distributions, and algebraic derivations of some of the most important results in statistics.

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