Serves as a comprehensive reference for health data scientists, bridging fundamental statistical principles with advanced analytical techniques. Provides clear explanations of statistical theory and its application to real-world health data.
Serves as a comprehensive reference for health data scientists, bridging fundamental statistical principles with advanced analytical techniques. Provides clear explanations of statistical theory and its application to real-world health data.
Dr Ding-Geng Chen is an elected fellow of the American Statistical Association and an elected member of the International Statistical Institute. Currently he is the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University. Dr. Chen has more than 250 referred professional publications and co-authored and co-edited 42 books on clinical trial methodology, meta-analysis, data science, causal inference, and public health statistics. Dr. Jeffrey Wilson is a Professor of Statistics and Biostatistics and serves as the Associate Dean of Research and Inclusive Excellence. His research focuses on statistical analysis of binary correlated data, and he has authored numerous peer-reviewed articles in the field. He has received several prestigious honors, including the 2024 Dr. Martin Luther King Jr. Faculty.
Inhaltsangabe
12. Marginal Models for Binary Longitudinal Outcomes with Time-Dependent Covariates. 13. Multiple Models for Binary Longitudinal Mixed-Model Effects. 14. Statistical Modeling of Survival Data Statistical. 15. Statistical Modeling with Bayesian Paradigm. 16. Jointly Modeling to Analyze Longitudinal and Survival Data with Bayesian Approach. 17. Nonlinear Regression. 18. Statistical Meta-Analysis. 19. Spatial Statistical Analysis. 20. Structural Equation Modeling. 21. Longitudinal Data Analysis and Latent Growth Curve Modelling. 22. Latent Growth Mixture Joint Modeling in Intervention Research. 23. Causal Inference and Propensity Score Analysis.
