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Full Description
In recent years, there has been a growing emphasis on making statistical methods and analytics accessible to health data science researchers and students. Following the first book on "Statistical Analytics for Health Data Science with SAS and R" (2023, http://www.routledge.com/9781032325620), this book serves as a comprehensive reference for health data scientists, bridging fundamental statistical principles with advanced analytical techniques. By providing clear explanations of statistical theory and its application to real-world health data, we aim to equip researchers with the necessary tools to navigate the evolving landscape of health data science.
Designed for advanced-level data scientists, this book covers a wide range of statistical methodologies, including models for longitudinal data with time-dependent covariates, multi-membership mixed-effects models, statistical modeling of survival data, Bayesian statistics, joint modeling of longitudinal and survival data, nonlinear regression, statistical meta-analysis, spatial statistics, structural equation modeling, latent growth curve modeling, causal inference and propensity score analysis.
A key feature of this book is its emphasis on real-world applications. We integrate publicly available health datasets and provide case studies from a variety of health applications. These practical examples demonstrate how statistical methods can be applied to solve critical problems in health science.
To support hands-on learning, we offer implementation guidance using SAS and R, ensuring that readers can replicate analyses and apply statistical techniques to their own research. Step-by-step computational examples facilitate reproducibility and deeper exploration of statistical models. By combining theoretical foundations with practical applications, this book empowers health data scientists to develop robust statistical solutions for complex health challenges. Whether working in academia, industry, or public health, readers will gain the expertise to advance data-driven decision-making and contribute to evidence-based health research.
Contents
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.
