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**Linear mixed models for longitudinal data.**
*(English)*
Zbl 0956.62055

Springer Series in Statistics. Berlin: Springer. xxii, 568 p. (2000).

This book is devoted to linear mixed-effects models with strong emphasis on the SAS procedure. Guidance and advice on practical issues are the main focus of the text. SAS version 6.12 was used throughout this book. The majority of the chapters is explanatory rather than research oriented and emphasizes practice rather than mathematical rigor. It is of value to applied statisticians and biomedical researchers. The book has 24 chapters and two appendices.

Chapter 1 is an introduction. The key examples, used throughout the book, are introduced in Chapter 2. Chapters 3 to 9 provide the core about the linear mixed-effects model, while Chapters 10 to 13 discuss more advanced tools for model exploration, influence diagnostics, as well as extensions of the original model. Chapters 14 to 16 introduce the reader to basic incomplete data concepts. Chapters 17 to 18 discuss strategies to model incomplete longitudinal data, based on the linear mixed model. The sensitivity of such strategies to parametric assumptions is investigated in Chapters 19 and 20. Some additional missing data topics are presented in Chapters 21 and 22. Chapter 23 is devoted to design considerations. Five case studies are treated in detail in Chapter 24. Appendix A reviews a number of software tools for fitting linear mixed models. Appendix B gives some technical details for sensitivity analysis.

I recommend this book as reference to applied statisticians and biomedical researchers, particularly in the pharmaceutical industry, medical and public organizations.

Chapter 1 is an introduction. The key examples, used throughout the book, are introduced in Chapter 2. Chapters 3 to 9 provide the core about the linear mixed-effects model, while Chapters 10 to 13 discuss more advanced tools for model exploration, influence diagnostics, as well as extensions of the original model. Chapters 14 to 16 introduce the reader to basic incomplete data concepts. Chapters 17 to 18 discuss strategies to model incomplete longitudinal data, based on the linear mixed model. The sensitivity of such strategies to parametric assumptions is investigated in Chapters 19 and 20. Some additional missing data topics are presented in Chapters 21 and 22. Chapter 23 is devoted to design considerations. Five case studies are treated in detail in Chapter 24. Appendix A reviews a number of software tools for fitting linear mixed models. Appendix B gives some technical details for sensitivity analysis.

I recommend this book as reference to applied statisticians and biomedical researchers, particularly in the pharmaceutical industry, medical and public organizations.

Reviewer: Wang Songgui (Beijing)

### MSC:

62J99 | Linear inference, regression |

62-01 | Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics |

62-02 | Research exposition (monographs, survey articles) pertaining to statistics |

62P10 | Applications of statistics to biology and medical sciences; meta analysis |