An introduction to multilevel regression models.
Data in health research are frequently structured hierarchically. For example, data may consist of patients nested within physicians, who in turn may be nested in hospitals or geographic regions. Fitting regression models that ignore the hierarchical structure of the data can lead to false inferences being drawn from the data. Implementing a statistical analysis that takes into account the hierarchical structure of the data requires special methodologies. In this paper, we introduce the concept of hierarchically structured data, and present an introduction to hierarchical regression models. We then compare the performance of a traditional regression model with that of a hierarchical regression model on a dataset relating test utilization at the annual health exam with patient and physician characteristics. In comparing the resultant models, we see that false inferences can be drawn by ignoring the structure of the data.
Austin
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PC
Institute for Clinical Evaluative Sciences, G-160, 2075 Bayview Avenue, North York, ON, M4N 3M5. peter.austin@ices.on.ca
Goel
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V
van Walraven
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C
D015982:Bias; D019411:Clinical Laboratory Techniques / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D005260:Female; D006302:Health Services Research / Q000379:methods*; D006801:Humans; D016014:Linear Models; D008297:Male; D016011:Normal Distribution; D009864:Ontario; D016012:Poisson Distribution; D011237:Predictive Value of Tests; D012044:Regression Analysis