An introduction to multilevel modeling for anesthesiologists.

Abstract

In population-based research, subjects are frequently in clusters with shared features or demographic characteristics, such as age range, neighborhood, who they have for a physician, and common comorbidities. Classification into clusters also applies at broader levels. Physicians are classified by physician group or by practice site; hospitals can be characterized by size, location, or demographics. Hierarchical, nested structures pose unique challenges in the conduct of research. Data from nested structures may be interdependent because of similarities among subjects in a cluster, while nesting at multiple levels makes it difficult to know whether findings should be applied to the individual or to the larger group. Statistical tools, known variously as hierarchical linear modeling, multilevel modeling, mixed linear modeling, and other terms, have been developed in the education and social science fields to deal effectively with these issues. Our goal in this article is to review the implications of hierarchical, nested data organization and to provide a step-by-step tutorial of how multilevel modeling could be applied to a problem in anesthesia research using current, commercially available software.