Variable Selection and Prediction Using a Nested, Matched Case-Control Study: Application to Hospital-Acquired Pneumonia in Stroke Patients

• Activity Number: 628
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Jing Qian*+ and Payabvash and Andre Kemmling and Michael Lev and Lee Schwamm and Rebecca A. Betensky
• Companies: University of Massachusetts and Massachusetts General Hospital and Massachusetts General Hospital and Massachusetts General Hospital and Massachusetts General Hospital and Harvard School of Public Health
• Keywords: AUC ; Cerebral infarction ; Elastic net ; Lasso ; Penalized likelihood ; ROC analysis

Abstract

Matched case-control designs are commonly used in epidemiologic studies for increased efficiency. These designs have recently been introduced to the setting of modern imaging and genomic studies, which are characterized by high-dimensional covariates. However, appropriate statistical analyses that adjust for the matching have not been widely adopted. A matched case-control study of 430 acute ischemic stroke patients was conducted at Massachusetts General Hospital (MGH) in order to identify specific brain regions of acute infarction that are associated with hospital acquired pneumonia (HAP) in these patients. We investigate penalized conditional logistic regression approaches to this variable selection problem that properly differentiate between selection of main effects and of interactions, and that acknowledge the matching. This neuroimaging study was nested within a larger prospective study of HAP which recorded clinical variables, but did not include neuroimaging. We demonstrate how the larger study, in conjunction with the nested, matched study, affords us the capability to derive a score for prediction of HAP in future stroke patients based on imaging and clinical features.