Multilevel models: applications to health data.
This paper presents an introductory account of multilevel models, highlighting the potential benefits that may be gained by the use of these methods. It draws on recent applications in health services research that have appeared in the literature. Methodological advances in these statistical techniques have taken place in the field of education, where empirical studies have mainly been concerned with comparing pupil achievement across different schools by exploring the relationship between individual and institutional factors. Although recent widespread availability of suitable software packages has enabled other disciplines to adopt these methods, to date they have received little attention in the health services research literature (the investigation of effects of geographical areas on health being a possible exception) despite their obvious application in many areas of current interest. Key areas that could benefit greatly from these techniques include the exploration of variations in clinical practice, comparisons of institutional performance and resource allocation.
Rice
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Centre for Health Economics, University of York, UK.
Leyland
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D015438:Health Behavior; D006302:Health Services Research / Q000379:methods*; D006305:Health Status Indicators; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D019984:Quality Indicators, Health Care; D012942:Social Sciences; D012984:Software; D006113:United Kingdom / Q000453:epidemiology