Statistical Literacy for Clinical Practitioners - Hardcover

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William H. Holmes has a Ph.D. in psychology from the University of Massachusetts at Amherst.  He is an associate professor of physician assistant studies and psychology at Le Moyne College, Syracuse, NY, and research coordinator for Le Moyne’s Department of Physician Assistant Studies.  Twice named the Instructor of the Year by Le Moyne’s Physician Assistant Student Society, Professor Holmes teaches statistics and technical writing to physician assistant students and co-mentors their master’s projects.

William C. Rinaman has a Ph.D. in statistics from American University.  He was a professor in the Department of Mathematics and Computer Science at Le Moyne College in Syracuse, NY, for 31 years.  He is now retired and living in Pinehurst, NC, where he enjoys playing golf and croquet.  He is currently doing volunteer tutoring and adjunct teaching at Sandhills Community College in Pinehurst.

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This textbook on statistics is written for students in medicine, epidemiology, and public health. It builds on the important role evidence-based medicine now plays in the clinical practice of physicians, physician assistants and allied health practitioners. By bringing research design and statistics to the fore, this book can integrate these skills into the curricula of professional programs. Students, particularly practitioners-in-training, will learn statistical skills that are required of today’s clinicians. Practice problems at the end of each chapter and downloadable data sets provided by the authors ensure readers get practical experience that they can then apply to their own work.

Topics covered include:

Functions of Statistics in Clinical Research

Common Study Designs

Describing Distributions of Categorical and Quantitative Variables

Confidence Intervals and Hypothesis Testing

Documenting Relationships in Categorical and Quantitative Data<

Assessing Screening and Diagnostic Tests

Comparing Means of Independent and Related Samples

Linear Regression

Binary Logistic Regression

Survival Analysis

Regression Analysis of Count Data