The book "Linear Mixed Models for Longitudinal Data" by Geert Verbeke and Geert Molenberghs is part of the Springer Series in Statistics. It provides a comprehensive guide to linear mixed models, which are widely used in various fields such as biostatistics, medical research, and public health. The authors, both experts in the field, have updated and expanded the content from their previous book, "Linear Mixed Models for Longitudinal Data" (1997), to address new research developments and practical issues. Key features of the book include: - **Practical Focus**: The book emphasizes practical application over theoretical rigor, making it accessible to applied statisticians and researchers. - **Software Integration**: While the first version heavily relied on the SAS MIXED procedure, this edition centralizes SAS-related issues in one chapter and includes examples using other software tools like MLwiN and SPlus. - **New Content**: The book covers a wide range of new topics, including advanced modeling techniques, sensitivity analysis, and the handling of incomplete data. - **Case Studies**: Several case studies are provided to illustrate the application of the models in real-world scenarios, such as the Baltimore Longitudinal Study of Aging, the Vorozole study, and the growth data of schoolgirls. - **Technical Details**: The book includes detailed technical discussions on estimation methods, inference, and model diagnostics, along with SAS code examples. The authors acknowledge the contributions of numerous researchers and acknowledge the support from various research grants and institutions. The book is intended for a broad audience, including applied statisticians, biomedical researchers, and professionals in the pharmaceutical industry and public health research organizations.The book "Linear Mixed Models for Longitudinal Data" by Geert Verbeke and Geert Molenberghs is part of the Springer Series in Statistics. It provides a comprehensive guide to linear mixed models, which are widely used in various fields such as biostatistics, medical research, and public health. The authors, both experts in the field, have updated and expanded the content from their previous book, "Linear Mixed Models for Longitudinal Data" (1997), to address new research developments and practical issues. Key features of the book include: - **Practical Focus**: The book emphasizes practical application over theoretical rigor, making it accessible to applied statisticians and researchers. - **Software Integration**: While the first version heavily relied on the SAS MIXED procedure, this edition centralizes SAS-related issues in one chapter and includes examples using other software tools like MLwiN and SPlus. - **New Content**: The book covers a wide range of new topics, including advanced modeling techniques, sensitivity analysis, and the handling of incomplete data. - **Case Studies**: Several case studies are provided to illustrate the application of the models in real-world scenarios, such as the Baltimore Longitudinal Study of Aging, the Vorozole study, and the growth data of schoolgirls. - **Technical Details**: The book includes detailed technical discussions on estimation methods, inference, and model diagnostics, along with SAS code examples. The authors acknowledge the contributions of numerous researchers and acknowledge the support from various research grants and institutions. The book is intended for a broad audience, including applied statisticians, biomedical researchers, and professionals in the pharmaceutical industry and public health research organizations.