Biostatistics in Public Health Using STATA

Striking a balance between theory, application, and programming, Biostatistics in Public Health Using STATA is a user-friendly guide to applied statistical analysis in public health using STATA version 14. The book supplies public health practitioners and students with the opportunity to gain expertise in the application of statistics in epidemiologic studies.

The book shares the authors? insights gathered through decades of collective experience teaching in the academic programs of biostatistics and epidemiology. Maintaining a focus on the application of statistics in public health, it facilitates a clear understanding of the basic commands of STATA for reading and saving databases.

The book includes coverage of data description, graph construction, significance tests, linear regression models, analysis of variance, categorical data analysis, logistic regression model, poisson regression model, survival analysis, analysis of correlated data, and advanced programming in STATA.

Each chapter is based on one or more research problems linked to public health. Additionally, every chapter includes exercise sets for practicing concepts and exercise solutions for self or group study. Several examples are presented that illustrate the applications of the statistical method in the health sciences using epidemiologic study designs.

Presenting high-level statistics in an accessible manner across research fields in public health, this book is suitable for use as a textbook for biostatistics and epidemiology courses or for consulting the statistical applications in public health.

For readers new to STATA, the first three chapters should be read sequentially, as they form the basis of an introductory course to this software.

Basic Commands. Data Description. Graph Construction. Significance Tests. Linear Regression Models. Analysis of Variance. Categorical Data Analysis. Logistic Regression Model. Poisson Regression Model. Survival Analysis. Analysis of Correlated Data. Introduction to Advanced Programming in STATA. References. Index.

Erick L. Suárez is a professor of biostatistics in the Department of Biostatistics and Epidemiology at the University of Puerto Rico Graduate School of Public Health. He has more than 25 years of experience teaching biostatistics at the graduate level and has co-authored more than 75 peer-reviewed publications in chronic and infectious diseases. Dr. Suarez has been a co-investigator of several NIH-funded grants related to cancer, HPV, HCV, and diabetes. He has extensive experience in statistical consulting with biomedical researchers, particularly in the analysis of microarrays data in breast cancer.

Cynthia M. Pérez is a professor of epidemiology in the Department of Biostatistics and Epidemiology at the University of Puerto Rico Graduate School of Public Health. She has taught epidemiology and biostatistics for over 20 years. She has also directed efforts in mentoring and training to public health and medical students at the University of Puerto Rico. She has been the principal investigator or co-investigator of research grants in diverse areas of public health including diabetes, metabolic syndrome, periodontal disease, viral hepatitis, and HPV infection. She is the author or co-author of more than 75 peer-reviewed publications.

Graciela M. Nogueras is a statistical analyst at the University of Texas MD Anderson Cancer Center in Houston, Texas. She is currently enrolled in the PhD program in biostatistics at the University of Texas—Graduate School of Public Health. She has co-authored more than 30 peer-reviewed publications. For the past nine years, she has been performing statistical analyses for clinical and basic science researchers. She has been assisting with the design of clinical trials and animal research studies, performing sample size calculations, and writing the clinical trial reports of clinical trial progress and interim analyses of efficacy and safety data to the University of Tex