Statistical Analytics for Health Data Science with SAS and R

1. Sampling and Data Collection
2. Measures of Tendency, Spread, Relative Standing, Association, Belief 3. Statistical Modeling of Mean of Continuous and Mean of Binary Outcomes
4. Modeling of Continuous and Binary Outcomes with Factors: One-way and Two-way ANOVA Models 5. Statistical Modeling of Continuous Outcomes with Continuous Explanatory Factors Linear Regression Models
6. Modeling Continuous Responses with Categorical and Continuous Covariates: One-Way Analysis of Covariance (ANCOVA)
7. Statistical Modeling of Binary Outcome with One or More Covariates: Standard Logistic Regression Model 8. Generalized Linear Models 9. Modeling Repeated Continuous Observations using GEE 10. Modeling for Correlated Continuous Responses with Random-Effects 11. Modeling Correlated Binary Outcomes through Hierarchical Logistic Regression Models

Advanced Statistical Analytics for Health Data Science with SAS and R

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.