Nonparametric analysis of longitudinal data in factorial experiments

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The authoritative reference on nonparametric methods for evaluating longitudinal data in factorial designs

Broadening the range of techniques that can be used to evaluate longitudinal data, Nonparametric Analysis of Longitudinal Data in Factorial Experiments presents nonparametric methods of evaluation that supplement the generalized linear models approach. Emphasizing the practical application of these methods in statistical procedures, this book provides a unified approach for the analysis of factorial designs involving longitudinal data that is appropriate for metric data, count data, ordered categorical data, and dichotomous data.

Topics covered include nonparametric models, effects and hypotheses in experimental design, estimators for relative effects, experiments for one and several groups of subjects, multifactorial experiments, dependent replications, and experiments with numerous time points. The basic mathematical principles for the methods introduced here are described in theory, consistent with the book's minimal math requirements. Simple approximations for small data sets are provided, as well as ample chapter exercises to test skills, an appendix that includes original data for the examples used throughout the book, and downloadable SAS-IML macros for implementing the more extensive calculations. All applications are designed to be useful in many fields.

Generously supplemented with more than 110 graphs and tables, Nonparametric Analysis of Longitudinal Data in Factorial Experiments is an essential reference for statisticians and biometricians, researchers in clinical trials, psychological studies, and in the fields of forestry, agriculture, sociology, ecology, and biology, as well as graduate students in statistics and biostatistics.

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Contents

Introduction
1
Models
21
Effects and Hypotheses
35
Copyright

11 other sections not shown

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About the author (2002)

EDGAR BRUNNER is Head of the Department of Medical Statistics at the University of Gottingen, Germany.

SEBASTIAN DOMHOF is Assistant Professor in the Department of Medical Statistics at the University of Gottingen, Germany.

FRANK LANGER is a Statistician at Lilly Deutschland GmbH, Bad Homburg, Germany.

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