title: Introduction to hierarchical modeling.
abstract: BACKGROUND
  Hierarchical modeling (HM) is a statistical technique that has gained in popularity in health care research. It has been used for analysis of secondary data, performance profiles or benchmarking studies, and in prospective trials. The technique is used in situations in which traditional regression analysis might lead to incorrect conclusions. Specifically, data drawn from nested settings such as hospital units or hospice providers may be correlated, thus violating an assumption required for ordinary least squares regression.
  
  
  OBJECTIVE
  This article provides a description of HM, reviews two recent articles in palliative care that have used the technique, and presents an illustrative case study to further illuminate the potential of the method.
  
  
  CONCLUSIONS
  When used appropriately, HM allows researchers to specify and test hypotheses that would not otherwise be possible, and avoid incorrect conclusions from nested data.
authors: Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA. degen@pitt.edu
authors: Howard B
authors: HB
authors: Degenholtz
authors: Mamta
authors: M
authors: Bhatnagar
mesh_terms: D000058:Accidental Falls; D005260:Female; D006302:Health Services Research / Q000706:statistics & numerical data*; D006738:Hospices; D006801:Humans; D008297:Male; D015233:Models, Statistical; D010166:Palliative Care