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J Clin Epidemiol. 2005 Apr;58(4):391-400.

Conventional models overestimate the statistical significance of volume-outcome associations, compared with multilevel models.

Author information

1
Institute for Clinical Evaluative Sciences, University of Toronto, G Wing Room 140, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada. david.urbach@ices.on.ca

Abstract

OBJECTIVE:

To compare the use of conventional statistical models with multilevel regression models in volume-outcome analyses of surgical procedures in an empirical case study.

STUDY DESIGN AND SETTING:

Using conventional regression models and multilevel regression models, we estimated the effect of hospital volume and surgeon volume on 30-day mortality and length of postoperative hospital stay in persons who had an esophagectomy, pancreaticoduodenectomy, or major lung resection for cancer in Ontario, Canada, from 1994 to 1999.

RESULTS:

The point estimates of volume-outcome associations were similar using either method; however, the 95% confidence intervals estimated by multilevel models were wider than those estimated by conventional models. A significant association between volume and mortality was identified in 2 of 18 (11%) comparisons using conventional analysis but in none of the 18 (0%) comparisons using multilevel analysis, and between volume and length of stay in 15 of 18 (83%) comparisons using conventional analysis and in 1 of 18 (6%) comparisons using multilevel analysis.

CONCLUSION:

Conventional and multilevel statistical models can yield substantially different results in the analysis of volume-outcome associations for surgical procedures.

PMID:
15862725
DOI:
10.1016/j.jclinepi.2004.12.001
[Indexed for MEDLINE]

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