Multilevel Modelling of Health Statistics

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.
∗ Focuses on multilevel modelling for health and medical research
∗ Covers the majority of analytical techniques required by health care professionals
∗ Repeated measures, outliers, institutional performance and spatial analysis are all examined in a multilevel framework
∗ All the methodology is applied to data sets relating to the health of individual or populations
∗ Includes in–depth discussion of the software packages used in multilevel modelling
From a basic introduction through to methodological extensions to generalised linear models, the book shows 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.
Health care professionals and public health researchers interested in the application of statistics will benefit greatly from this text. It will also be of interest to postgraduate students studying medical statistics.

Spis treści:
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. Spiegelhal ter).
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.

Nota biograficzna:
A. H. Leyland Public Health Research Unit, University of Glasgow, UK
H. Goldstein Institute of Education, University of London, UK

Okładka tylna:
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.
∗ Focuses on multilevel modelling for health and medical research
∗ Covers the majority of analytical techniques required by health care professionals
∗ Repeated measures, outliers, institutional performance and spatial analysis are all examined in a multilevel framework
∗ All the methodology is applied to data sets relating to the health of individual or populations
∗ Includes in–depth discussion of the software packages used in multilevel modelling
From a basic introduction through to methodological extensions to generalised linear models, the book shows 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.
Health care professionals and public health researchers interested in the application of statistics will benefit greatly from this text. It will also be of interest to postgraduate students studying medical statistics.