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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
Authors:Email author" target="_blank">Christopher?S?CoffeyEmail author  Patricia?R?Hebert  Marylyn?D?Ritchie  Harlan?M?Krumholz  J?Michael?Gaziano  Paul?M?Ridker  Nancy?J?Brown  Douglas?E?Vaughan  Jason?H?Moore
Institution:(1) Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294-0022, USA;(2) Section of Cardiovascular Medicine, Department of Medicine, Yale University School of Medicine, New Haven, CT 06510, USA;(3) Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN 37232-0700, USA;(4) Section of Health Policy and Administration, Department of Epidemiology and Public Health and Robert Wood Johnson Clinical Scholars Program, Yale University School of Medicine, New Haven, CT 06510, USA;(5) Yale-New Haven Hospital Center for Outcomes Research and Evaluation, New Haven, CT 06510, USA;(6) Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA;(7) Center for Cardiovascular Disease Prevention, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA;(8) Departments of Medicine and Pharmacology, Vanderbilt University Medical School, Nashville, TN 37232-0700, USA
Abstract:

Background  

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.
Keywords:
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