Description
Book SynopsisMultilevel 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.
Trade Review"...contains 13 well written chapters by experts...the references are recent and useful. It can be used as a textbook in graduate level modeling course." (Journal of Statistical Computation & Simulation, May 2004)
"...exhibits a marvellous degree of coherence and clarity..." (Pharmaceutical Statistics, Vol 2, 2003)
"...good introductions to multilevel models, and plenty of examples..." (Zentralblatt Math, 2003)
"...I believe that the book all in all fulfils this promise..." (Statistics in Medicine, No.21, 2002)
"...a very readable book whose audience does not seem to be limited to statisticians." (Technometrics, Vol. 44, No. 4, November 2002)
"Highly recommended to biostatisticians, health care professionals and public health researchers in the application of multilevel model. It can also be used as a reference book for postgraduate students studying medical statistics." (ISCB News, December 2001)
Table of ContentsPreface.
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.
