Mixed Effects Models for Complex Data

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data....Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when na.ve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Pages: 431

Series: Monographs on Statistics and Applied Probability

Volume: 113

Edition: 01

Publisher: Taylor & Francis, Incorporated

Copyright: 2010

Published: 11/09