Group variable selection in cardiopulmonary cerebral resuscitation data for veterinary patients

Young Joo Yoon, Cheolwoo Park, Erik Hofmeister, Sangwook Kang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Cardiopulmonary cerebral resuscitation (CPCR) is a procedure to restore spontaneous circulation in patients with cardiopulmonary arrest (CPA). While animals with CPA generally have a lower success rate of CPCR than people do, CPCR studies in veterinary patients have been limited. In this paper, we construct a model for predicting success or failure of CPCR, and identifying and evaluating factors that affect the success of CPCR in veterinary patients. Due to reparametrization using multiple dummy variables or close proximity in nature, many variables in the data form groups, and thus a desirable method should take this grouping feature into account in variable selection. To accomplish these goals, we propose an adaptive group bridge method for a logistic regression model. The performance of the proposed method is evaluated under different simulated setups and compared with several other regression methods. Using the logistic group bridge model, we analyze data from a CPCR study for veterinary patients and discuss their implications on the practice of veterinary medicine.

Original languageEnglish
Pages (from-to)1605-1621
Number of pages17
JournalJournal of Applied Statistics
Issue number7
Publication statusPublished - 2012 Jul

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Group variable selection in cardiopulmonary cerebral resuscitation data for veterinary patients'. Together they form a unique fingerprint.

Cite this