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The book "Models for Multi-State Survival Data: Rates, Risks, and Pseudo-Values" by Per Kragh Andersen and Henrik Ravn focuses on modeling and analyzing multistate processes in biomedical or industrial settings. The authors use small biomedical datasets to illustrate modeling choices and estimation procedures, presenting the material in a practical and accessible manner. The book covers various intensity models, estimation procedures, and marginal modeling approaches, providing a valuable summary of a research area active for the past 50 years. [Extracted from the article]

"Statistical Methods in Health Disparity Research" by J. Sunil Rao is a comprehensive guide for statisticians working in biomedical, health services, epidemiological, or health policy fields who want to understand and address health disparities. The book covers basic concepts, essential statistical methods, small area estimation, causality, moderation, mediation modeling, modern machine learning approaches, precision medicine, and other extended topics. While the book provides useful technical guidance and substantive context, readers may need to consult additional sources for more in-depth understanding of certain approaches. Overall, the book is a valuable reference for researchers interested in health disparities. [Extracted from the article]

"Statistical Analytics for Health Data Science with SAS and R" by Jeffrey R. Wilson, Ding-Geng Chen, and Karl E. Peace is a practical and user-friendly guide to statistics and health data analysis. The book focuses on downstream analyses and provides hands-on tools rather than complex statistical theories. It organizes its chapters around scientific questions, making it engaging for students in Statistics, Biostatistics, and Computational Biology programs. The book covers a wide range of statistical methods, from foundational concepts to advanced techniques, and includes real-world datasets for authenticity. Overall, it is a valuable resource for applied data scientists, graduate students, and public health researchers. [Extracted from the article]

The book is organized in two parts and eighteen chapters: the first part considers "Preparation for Analysis" (Chapters 1-6), and the second part is called "Regression Analysis" (Chapters 7-18) although it covers many other related methods as well. Added in this edition new Chapter 4 discusses various methods of data visualization with specific tools for different types of bivariate and multivariate variables, and gives multiple illustrations in graphs and figures. Chapter 7 presents simple linear regression, its residual errors, confidence intervals, parameter hypotheses testing, correlation, fixed predictor variable, bivariate regression as the conditional distribution. [Extracted from the article]

Large sample theory plays a critical role in the development and application of statistical methodology. Jiang is the author of two previous textbooks, I Linear and Generalized Linear Mixed Models and Their Applications i (Jiang, [1]) and I Large Sample Techniques for Statistics i (Jiang, [2]). Since small area estimation largely relies on mixed effect modeling, many properties mentioned in other chapters (such as model prediction covered in Chapter 2) can be tailored to small area estimation. [Extracted from the article]

Measurement error is an important and challenging topic affecting all areas in biostatistics, including survival analysis, longitudinal data analysis, and causal inference, among others. The other method used in this chapter is the correction of conditional expectation on the likelihood function, which is similar to the insertion correction in chapter 3. Different from topics in chapters 3 to 5, which are frequently explored in research papers and are discussed in some measurement error textbooks, chapters 6 and 7 include more novel topics that have not been fully discussed in other measurement error textbooks. [Extracted from the article]

The authors frequently mention the advantages of using either PROC MIXED or PROC GLIMMIX over other SAS procedures for these analyses. The book focuses exclusively on the use of PROC MIXED and PROC GLIMMIX, and it shows some of the great versatility of these two SAS procedures. In various places, the authors describe older techniques that were used before PROC MIXED and/or PROC GLIMMIX were introduced, and they carefully spell out the limitations of these techniques. [Extracted from the article]

Reviews the book "Generalized Estimating Equations," by James W. Hardin and Joseph M. Hilbe.

The article reviews the book "Numerical Methods for Nonlinear Estimating Equations," by Christopher G. Small and Jinfang Wang.

Reviews the book "Generalized Estimating Equations," by James W. Hardin and Joseph M. Hilbe.