Volume 20, Issue 16 pp. 2505-2532
Tutorial in Biostatistics

Using observational data to estimate prognosis: an example using a coronary artery disease registry

Elizabeth R. DeLong

Corresponding Author

Elizabeth R. DeLong

Outcomes Research & Assessment Group, Duke Clinical Research Institute, Duke University, Department of Medicine, Biometry Division, Community and Family Medicine, 2400 Pratt Street, Durham, NC 27705, U.S.A.

Outcomes Research & Assessment Group, Duke Clinical Research Institute, Duke University, Department of Medicine, Biometry Division, Community and Family Medicine, 2400 Pratt Street, Durham, NC 27705, U.S.A.Search for more papers by this author
Charlotte L. Nelson

Charlotte L. Nelson

Outcomes Research & Assessment Group, Duke Clinical Research Institute, Duke University, Department of Medicine, Durham, NC 27710-7510, U.S.A.

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John B. Wong

John B. Wong

New England Medical Center, Department of Medicine, Box 302, Boston, MA 02111, U.S.A.

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David B. Pryor

David B. Pryor

Allina Health System, P.O. Box 9310, Minneapolis, MN 55440-9310, U.S.A.

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Eric D. Peterson

Eric D. Peterson

Outcomes Research & Assessment Group, Duke Clinical Research Institute, Duke University, Department of Medicine, Durham, NC 27710-7510, U.S.A.

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Kerry L. Lee

Kerry L. Lee

Duke Clinical Research Institute, Biometry Division, Community and Family Medicine, Box 3363, Durham, NC 27710-7510, U.S.A.

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Daniel B. Mark

Daniel B. Mark

Outcomes Research & Assessment Group, Duke Clinical Research Institute, Duke University, Department of Medicine, Durham, NC 27710-7510, U.S.A.

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Robert M. Califf

Robert M. Califf

Duke Clinical Research Institute, Duke University Division of Cardiology and Department of Medicine, Box 31123, Durham, NC 27710-7510, U.S.A.

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Stephen G. Pauker

Stephen G. Pauker

New England Medical Center, Department of Medicine, Box 302, Boston, MA 02111, U.S.A.

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First published: 10 August 2001
Citations: 20

Abstract

With the proliferation of clinical data registries and the rising expense of clinical trials, observational data sources are increasingly providing evidence for clinical decision making. These data are viewed as complementary to randomized clinical trials (RCT). While not as rigorous a methodological design, observational studies yield important information about effectiveness of treatment, as compared with the efficacy results of RCTs. In addition, these studies often have the advantage of providing longer-term follow-up, beyond that of clinical trials. Hence, they are useful for assessing and comparing patients' long-term prognosis under different treatment strategies. For patients with coronary artery disease, many observational comparisons have focused on medical therapy versus interventional procedures. In addition to the well-studied problem of treatment selection bias (which is not the focus of the present study), three significant methodological problems must be addressed in the analysis of these data: (i) designation of the therapeutic arms in the presence of early deaths, withdrawals, and treatment cross-overs; (ii) identification of an equitable starting point for attributing survival time; (iii) site to site variability in short-term mortality. This paper discusses these issues and suggests strategies to deal with them. A proposed methodology is developed, applied and evaluated on a large observational database that has long-term follow-up on nearly 10 000 patients. Copyright © 2001 John Wiley & Sons, Ltd.

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