Wan Tang (Ph.D.) is a Clinical Professor in the Department of Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine. Dr. Tang's research interests include longitudinal data analysis, missing data modeling, structural equation models, causal inference, and nonparametric smoothing methods. He has co-edited a book on modern clinical trials.

Hua He (Ph.D.) is an Associate Professor in Biostatistics in the Department of Epidemiology at Tulane University School of Public Health and Tropical Medicine. Dr. He is a highly experienced biostatistician with expertise in longitudinal data analysis, structural equation models, potential outcome based causal inference, semiparametric models, ROC analysis and their applications to observational studies, and randomized controlled trials across a range of disciplines, especially in the behavioral and social sciences. She has co-authored a series of publications in peer-reviewed journals, one textbook on categorical data analysis and co-edited a book on statistical causal inference and their applications in public health research.

Xin Tu (Ph.D.) is a Professor in the Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, UCSD. Dr. Tu is well versed in statistical methods and their applications to a range of disciplines, particularly within the fields of biomedical, behavioral and social sciences. He has co-authored over 300 peer-reviewed publications, two textbooks on categorical data and applied U-statistics, and co-edited books on modern clinical trials and social network data analysis. He has done important work in the areas of longitudinal data analysis, causal inference, U-statistics, survival analysis with interval censoring and truncation, pooled testing, semiparametric efficiency, and has successfully applied his novel development to addressing important methodological problems in biomedical and psychosocial research.

1. Introduction 2. Contingency Tables 3. Sets of Contingency Tables 4. Regression Models for Binary Response 5. Regression Models for Polytomous Responses 6. Regression Models for Count Response 7. Log-Linear Models for Contingency Tables 8. Analyses of Discrete Survival Time 9. Longitudinal and Clustered Data Analysis 10. Evaluation of Instruments 11. Analysis of Incomplete Data