Multilevel modelling of health statistics
著者
書誌事項
Multilevel modelling of health statistics
(Wiley series in probability and mathematical statistics)
John Wiley, c2001
大学図書館所蔵 全30件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Bibliography: p. [205]-212
Includes index
内容説明・目次
内容説明
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
目次
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.
「Nielsen BookData」 より