Penalized Count Data Regression with Application to Hospital Stay After Pediatric Cardiac Surgery

• Activity Number: 504
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Zhu Wang*+ and Shuangge Ma and Michael Zappitelli and Chirag Parikh and Ching-Yun Wang and Prasad Devarajan
• Companies: Connecticut Children's Medical Center and Yale and McGill University Health Centre and Yale and Fred Hutchinson Cancer Research Center and Cincinnati Children's Hospital Medical Center
• Keywords: Poisson regression ; negative binomial regression ; variable selection ; Enet ; Mnet ; Snet

Abstract

Pediatric cardiac surgery may lead to poor outcomes such as acute kidney injury (AKI) and prolonged hospital length of stay (LOS). Plasma and urine biomarkers may help with early identification and prediction of these adverse clinical outcomes. In a recent multi-center study, 311 children undergoing cardiac surgery were enrolled to evaluate multiple biomarkers for diagnosis and prognosis of AKI and other clinical outcomes. LOS is often analyzed as count data, thus Poisson regression and negative binomial (NB) regression are frequently used in practice. The present paper proposes new variable selection methods for Poisson and NB regression. We evaluated regularized regression through penalized likelihood function. We develop a unified algorithm to estimate the parameters and conduct variable selection simultaneously. Simulation studies show that the proposed methods have advantages with highly correlated predictors, against some of the competing methods. Applying the proposed methods to the aforementioned data, it is discovered that early postoperative urine biomarkers including NGAL, IL18 and KIM-1 independently predict LOS, after adjusting for risk and biomarker variables.