Analysis of Longitudinal Data

"The new edition of this important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving and important area of biostatistics. Two new chapters have been added on fully parametric models for discrete repeated measures data and on statistical models for time-dependent predictors where there may be feedback between the predictor and response variables. It also contains the many useful features of the previous edition such as, design issues, exploratory methods of analysis, linear models for continuous data, and models and methods for handling data and missing values."--OUP. The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical sciences. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values. Under each heading, worked examples are presented in parallel with the methodological development, and sufficient detail is given to enable the reader to reproduce the author's results using the data-sets as an appendix. This second edition, published for the first time in paperback, provides a thorough and expanded revision of this important text. It includes two new chapters; the first discusses fully parametric models for discrete repeated measures data, and the second explores statistical models for time-dependent predictors.

Aiming their work at the first year postgraduate statistic students, the authors (from Lancaster U., the U. of Washington, and Johns Hopkins U.) describe statistical models and methods for the analysis of longitudinal data, strongly emphasizing applications in biological and health sciences. After covering basic issues of design and exploratory analysis, they develop linear models and associated statistical methods for data sets in which the response variable is a continuous measurement. Later chapters examine generalized linear models for discrete response variables. Also explored are stochastic processes that may interact with response processes, solutions to the problems of missing values in longitudinal studies, and additional topics. Annotation c. Book News, Inc., Portland, OR