This book describes statistical models and methods for the analysis of longitudinal data, with a strong emphasis on applications in the biological and health sciences. The technical level of the book is roughly that of a final year undergraduate or first year postgraduate course in statistics. The book divides naturally into three blocks. The first three chapters provide an introduction to the subject, and cover basic issues of design and exploratory analysis. Chapters 4, 5, 6, and 11 develop linear models and associated statistical methods for data sets in which the response variable is a continuous measurement. Chapters 7, 8, 9, and 10 are concerned with generalized linear models for discrete response variables. Appendix A gives a brief review of the statistical background assumed. This book is intended for statisticians - MSc students and researchers.

Review:

. . . provides an excellent bridge between novel concepts in theoretical statistics and their potential use in applied research. ( Statistics in Medicine, 23 )

The topics covered are too numerous to dwell on here ... If your work involves longitudinal data and you wish to update, this book will serve you very well. As a quick look-up, it is very useful. ( Pharmaceutical Statistics )

The authors conclude each chapter with a helpful summary or conclusion, often indicating further reading. Helpfully, they also mention the topics that they have chosen not to present, together with other recommended books for you to follow up ... They have also chosen a good selection of examples, many of them medical, with which the various methods are clearly illustrated. ( Pharmaceutical Statistics )

Readers with interests across a wide spectrum of application areas will find the ideas relevant and interesting ... The book is readable and well written ... It belongs to the possession of every statistician who encounters longitudinal data. ( Zentralblatt MATH )

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Analysis of Longitudinal Data

Peter J. Diggle; Kung-Yee Liang; Scott L. Zeger

Used - Hardcover

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