Variable selection and prediction using a nested, matched case-control study: Application to hospital acquired pneumonia in stroke patients

Jing Qian, Seyedmehdi Payabvash, André Kemmling, Michael H. Lev, Lee H. Schwamm, Rebecca A. Betensky

Research output: Contribution to journalArticlepeer-review


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. There are 138 brain regions in which infarction was measured, which introduce nearly 10,000 two-way interactions, and challenge the statistical analysis. We investigate penalized conditional and unconditional 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 in 1915 stroke patients at MGH, 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. We evaluate the proposed methods in simulation studies and we apply them to the MGH HAP study.

Original languageEnglish (US)
Pages (from-to)153-163
Number of pages11
Issue number1
StatePublished - Mar 2014


  • AUC
  • Cerebral infarction
  • Conditional logistic regression
  • Elastic net
  • Lasso
  • Penalized likelihood
  • ROC analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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