Biostatistics for Clinical and Public Health Research

The new edition of Biostatistics for Clinical and Public Health Research is an introductory workbook to provide not only a concise overview of key statistical concepts but also step-by-step guidance on how to apply these through a range of software packages, including R, SAS, and Stata. Providing a comprehensive survey of essential topics - including probability, diagnostic testing, probability distributions, estimation, hypothesis testing, correlation, regression, and survival analysis - each chapter features a detailed summary of the topic at hand, followed by examples to show readers how to conduct analysis and interpret the results. Also including exercises and solutions, case studies, take-away points, and data sets (Excel, SAS, and Stata formats), the new edition now includes a chapter on data literacy and data ethics, as well as examples drawn from the COVID-19 pandemic. Ideally suited to accompany either a course or as support for independent study, this book will be an invaluable tool for both students of biostatistics and clinical or public health practitioners.

Melody S. Goodman is a professor in the Department of Biostatistics at New York University School of Global Public Health. She is a biostatistician with experience in study design, developing survey instruments, data collection, data management, and data analysis for public health and clinical research projects. She has taught introductory biostatistics for masters of public health and medical students for over 15 years at multiple institutions (Stony Brook University School of Medicine, Washington University in St. Louis School of Medicine, New York University School of Global Public Health).

Introduction. 1: Descriptive Statistics. Lab A1: Introduction to R/RStudio. Lab A2: Introduction to SAS. Lab A3: Introduction to Stata. 2: Probability. 3: Diagnostic Testing. 4: Discrete Probability Distributions. 5: Continuous Probability Distributions. Lab B: Probability Distributions. 6: Estimation. 7: One-Sample Hypothesis Testing. Lab C: One-Sample Hypothesis Testing Including Power and Sample Size. 8: Two-Sample Hypothesis Testing. 9: Nonparametric Hypothesis Testing. Lab D: Two-Sample Hypothesis Testing and Nonparametric Methods. 10: Hypothesis Testing for Categorical Data. 11: One-Way Analysis of Variance (ANOVA). 12: Correlation. 13: Linear Regression. 14: Logistic Regression. 15: Survival Analysis. Lab E: Data Analysis Project. 16: The Importance of Data Literacy and Data Ethics.

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