208 Pages 48 B/W Illustrations
    by CRC Press

    206 Pages 48 B/W Illustrations
    by CRC Press

    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
    Entering STATA
    STATA Working Directories
    Reading a Data File
    insheet Procedure
    Types of Files
    Data Editor

    Data Description
    Most Useful Commands
    list Command
    Mathematical and Logical Operators
    generate Command
    recode Command
    drop Command
    replace Command
    label Command
    summarize Command
    do-file Editor
    Descriptive Statistics and Graphs
    tabulate Command

    Graph Construction
    Box Plot
    Bar Chart

    Significance Tests
    Normality Test
    Variance Homogeneity
    Student’s t-Test for Independent Samples
    Confidence Intervals for Testing the Null Hypothesis
    Nonparametric Tests for Unpaired Groups
    Sample Size and Statistical Power

    Linear Regression Models
    Model Assumptions
    Parameter Estimation
    Hypothesis Testing
    Coefficient of Determination
    Pearson Correlation Coefficient
    Scatter Plot
    Running the Model
    Multiple Linear Regression Model
    Partial Hypothesis
    Polynomial Linear Regression Model
    Sample Size and Statistical Power
    Considerations for the Assumptions of the Linear Regression Model

    Analysis of Variance
    Data Structure
    Example for Fixed Effects
    Linear Model with Fixed Effects
    Analysis of Variance with Fixed Effects
    Programming for ANOVA
    Planned Comparisons (before Observing the Data)
    Multiple Comparisons: Unplanned Comparisons
    Random Effects
    Other Measures Related to the Random Effects Model
    Example of a Random Effects Model
    Sample Size and Statistical Power

    Categorical Data Analysis
    Cohort Study
    Case-Control Study
    Sample Size and Statistical Power

    Logistic Regression Model
    Model Definition
    Parameter Estimation
    Programming the Logistic Regression Model
    Alternative Database
    Estimating the Odds Ratio
    Significance Tests
    Extension of the Logistic Regression Model
    Adjusted OR and the Confounding Effect
    Effect Modification
    Prevalence Ratio
    Nominal and Ordinal Outcomes
    Sample Size and Statistical Power

    Poisson Regression Model
    Model Definition
    Relative Risk
    Parameter Estimation
    Programming the Poisson Regression Model
    Assessing Interaction Terms

    Survival Analysis
    Probability of Survival
    Components of the Study Design
    Kaplan–Meier Method
    Programming of S(t)
    Hazard Function
    Relationship between S(t) and h(t)
    Cumulative Hazard Function
    Median Survival Time and Percentiles
    Comparison of Survival Curves
    Proportional Hazards Assumption
    Significance Assessment
    Cox Proportional Hazards Model
    Assessment of the Proportional Hazards Assumption
    Survival Function Estimation Using the Cox Proportional Hazards Model
    Stratified Cox Proportional Hazards Model

    Analysis of Correlated Data
    Regression Models with Correlated Data
    Mixed Models
    Random Intercept
    Using the mixed and gllamm Commands with a Random Intercept
    Using the mixed Command with Random Intercept and Slope
    Mixed Models in a Sampling Design

    Introduction to Advanced Programming in STATA
    program Command
    Log Files
    trace Command
    Local Macros
    Loops (foreach and forvalues)
    Application of matrix and local Commands for Prevalence




    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 Texas MD Anderson Data and Safety Monitoring Board.

    Camille Moreno-Gorrín is a graduate of the Master of Science Program in Epidemiology at the University of Puerto Rico Graduate School of Public Health. During her graduate studies, she was a research assistant at the Comprehensive Cancer Center of the University of Puerto Rico where she co-authored several articles in biomedical journals. She also worked as a research coordinator for the HIV/AIDS Surveillance System of the Puerto Rico Department of Health, where she conducted research on intervention programs to link HIV patients to care.