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Adjustments for Center in Multicenter Studies: An Overview

A. Russell Localio, JD, MS; Jesse A. Berlin, ScD; Thomas R. Ten Have, PhD; and Stephen E. Kimmel, MD, MS
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From University of Pennsylvania, Philadelphia, Pennsylvania.

Grant Support: In part by the Agency for Healthcare Research and Quality, Centers for Education and Research on Therapeutics, University of Pennsylvania School of Medicine (U18 HS10399).

Corresponding Author: A. Russell Localio, JD, MS, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, 606 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021; e-mail, rlocalio@cceb.upenn.edu.

Current Author Addresses: Drs. Localio, Berlin, Ten Have, and Kimmel: Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, 606 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021.

Ann Intern Med. 2001;135(2):112-123. doi:10.7326/0003-4819-135-2-200107170-00012
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Increasingly, investigators rely on multicenter or multigroup studies to demonstrate effectiveness and generalizability. Authors too often overlook the analytic challenges in these study designs: the correlation of outcomes and exposures among patients within centers, confounding of associations by center, and effect modification of treatment or exposure across center. Correlation or clustering, resulting from the similarity of outcomes among patients within a center, requires an adjustment to confidence intervals and P values, especially in observational studies and in randomized multicenter studies in which treatment is allocated by center rather than by individual patient. Multicenter designs also warrant testing and adjustment for the potential bias of confounding by center, and for the presence of effect modification or interaction by center. This paper uses examples from the recent biomedical literature to highlight the issues and analytic options.





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