Douglas E Vaughan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Douglas E Vaughan has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Charlotte L. Phillips
Departments of Biochemistry, University of Missouri, Columbia, MO, 65211, USA; Child Health(,) University of Missouri, Columbia, MO, 65211, USA
1 shared publication - Christopher S. Coffey
Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294-0022, USA
1 shared publication - Harlan M. Krumholz
Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
1 shared publication - J. Michael Gaziano
Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA 02215-1204, USA
1 shared publication - Jason H. Moore
Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, USA
1 shared publication M. Eren
Departments of Medicine and Pharmacology, Vanderbilt University, Nashville Tennessee
1 shared publication
Latest Publications
- Open Access
- 30 April 2004
- Published by Springer Nature in BMC Bioinformatics
- Vol. 5 (1) , 49
An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene Interactions on risk of myocardial infarction: The importance of model validation
To examine interactions among the angiotensin converting enzyme (ACE) insertion/deletion, plasminogen activator inhibitor-1 (PAI-1) 4G/5G, and tissue plasminogen activator (t-PA) insertion/deletion gene polymorphisms on risk of myocardial infarction using data from 343 matched case-control pairs from the Physicians Health Study. We examined the data using both conditional logistic regression and the multifactor dimensionality reduction (MDR) method. One advantage of the MDR method is that it provides an internal prediction error for validation. We summarize our use of this internal prediction error for model validation. The overall results for the two methods were consistent, with both suggesting an interaction between the ACE I/D and PAI-1 4G/5G polymorphisms. However, using ten-fold cross validation, the 46% prediction error for the final MDR model was not significantly lower than that expected by chance. The significant interaction initially observed does not validate and may represent a type I error. As data-driven analytic methods continue to be developed and used to examine complex genetic interactions, it will become increasingly important to stress model validation in order to ensure that significant effects represent true relationships rather than chance findings.
- 30 April 2004
- Published by Springer Nature in BMC Bioinformatics
- Vol. 5 (1) , 49
Open AccessAn application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene Interactions on risk of myocardial infarction: The importance of model validation
To examine interactions among the angiotensin converting enzyme (ACE) insertion/deletion, plasminogen activator inhibitor-1 (PAI-1) 4G/5G, and tissue plasminogen activator (t-PA) insertion/deletion gene polymorphisms on risk of myocardial infarction using data from 343 matched case-control pairs from the Physicians Health Study. We examined the data using both conditional logistic regression and the multifactor dimensionality reduction (MDR) method. One advantage of the MDR method is that it provides an internal prediction error for validation. We summarize our use of this internal prediction error for model validation. The overall results for the two methods were consistent, with both suggesting an interaction between the ACE I/D and PAI-1 4G/5G polymorphisms. However, using ten-fold cross validation, the 46% prediction error for the final MDR model was not significantly lower than that expected by chance. The significant interaction initially observed does not validate and may represent a type I error. As data-driven analytic methods continue to be developed and used to examine complex genetic interactions, it will become increasingly important to stress model validation in order to ensure that significant effects represent true relationships rather than chance findings.