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Biostatistics in Public Health Using Stata

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Table of Contents

Basic Commands Introduction Entering STATA Taskbar Help 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 Introduction Box Plot Histogram Bar Chart Significance Tests Introduction 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 Introduction Model Assumptions Parameter Estimation Hypothesis Testing Coefficient of Determination Pearson Correlation Coefficient Scatter Plot Running the Model Centering Bootstrapping Multiple Linear Regression Model Partial Hypothesis Prediction Polynomial Linear Regression Model Sample Size and Statistical Power Considerations for the Assumptions of the Linear Regression Model Analysis of Variance Introduction 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 Introduction 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 Overdispersion Sample Size and Statistical Power Poisson Regression Model Model Definition Relative Risk Parameter Estimation Example Programming the Poisson Regression Model Assessing Interaction Terms Overdispersion Survival Analysis Introduction 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 Introduction do-files program Command Log Files trace Command Delimiters Indexing Local Macros Scalars Loops (foreach and forvalues) Application of matrix and local Commands for Prevalence Estimation References Index

About the Author

Erick L. Suarez 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. Perez 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-Gorrin 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.

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