Multilevel Modelling of Health Statistics

by Leyland, A. H.; Goldstein, Harvey

• ISBN13: 9780471998907
• ISBN10: 0471998907
• Edition: 1st
• Format: Hardcover
• Copyright: 2001-03-30
• Publisher: WILEY

Summary

Multilevel modelling facilitates the analysis of hierarchical data where observations may be nested within higher levels of classification. In health care research, for example, a study may be undertaken to determine the variability of patient outcomes where these also vary by hospital or health care region. Inference can then be made on the efficacy of health care practices. This book provides the reader with the analytical techniques required to study such data sets. * First book to focus on multilevel modelling for health and medical research * Covers the majority of analytical techniques required by health care professionals * Unifies the literature on multilevel modelling for medical and health researchers * Each contribution comes from a specialist in that area Guiding the reader through various stages, from a basic introduction through to methodological extensions and generalised linear models, this test will show how various kinds of data can be analysed in a multilevel framework. Important statistical concepts, such as sampling and outliers, are covered specifically for multilevel data. Repeated measures, outliers, institutional performance, and spatial analysis, which have great relevance to health and medical research, are all examined for multilevel models. The book is aimed at health care professionals and public health researchers interested in the application of statistics, and will also be of interest to postgraduate students studying medical statistics. Wiley Series in Probability and Statistics

Author Biography

<B>A. H. Leyland</B> Public Health Research Unit, University of Glasgow, UK

Table of Contents

Preface.

Contributors.

Introduction.

Multilevel Data and Their Analysis (M. Healy).

Modelling Repeated Measurements (H. Glodstein and G. Woodhouse).

Binomial Regression (N. Rice).

Poisson Regression (I. Langford and R. Day).

Multivariate Multilevel Models (A. McLeod).

Outliers, Robustness and the Detection of Discrepant Data (T. Lewis and I. Langford).

Modelling Non-Hierarchical Structures (J. Rasbash and W. Browne).

Multinomial Regression (M. Yang).

Institutional Performance (E. Marshall and D. Spiegelhalter).

Spatial Analysis (A. Leyland).

Sampling (T. Snijders).

Further Topics in Multilevel Modelling (H. Goldstein and A. Leyland).

Software for Multilevel Analysis (J. de Leeuw and I. Kreft).

References.

Index.