title: Statistical and substantive inferences in public health: issues in the application of multilevel models.
abstract: Multilevel statistical models have become increasingly popular among public health researchers over the past decade. Yet the enthusiasm with which these models are being adopted may obscure rather than solve some problems of statistical and substantive inference. We discuss the three most common applications of multilevel models in public health: (a) cluster-randomized trials, (b) observational studies of the multilevel etiology of health and disease, and (c) assessments of health care provider performance. In each area of investigation, we describe how multilevel models are being applied, comment on the validity of the statistical and substantive inferences being drawn, and suggest ways in which the strengths of multilevel models might be more fully exploited. We conclude with a call for more careful thinking about multilevel causal inference.
authors.affiliation: School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, USA. bartbing@umich.edu
authors.forename: Jeffrey B
authors.initials: JB
authors.lastname: Bingenheimer
authors.forename: Stephen W
authors.initials: SW
authors.lastname: Raudenbush
mesh_terms: D001499:Bayes Theorem; D015984:Causality; D016000:Cluster Analysis; D004812:Epidemiologic Methods; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D011634:Public Health; D016032:Randomized Controlled Trials as Topic