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Published on: October 23, 2020
EM for regularized zero-inflated regression models with applications to postoperative morbidity after cardiac surgery
Zhu Wang1, Shuangge Ma, Ching-Yun Wang
1Department of Research, Connecticut Children's Medical Center, Department of Pediatrics, University of Connecticut School of Medicine, Hartford, CT, U.S.A.
Insights
This study introduces a novel statistical method for predicting pediatric cardiac surgery complications. The new approach improves risk factor identification for postoperative morbidity, enhancing patient care.
Area of Science:
- Biostatistics
- Pediatric Cardiac Surgery
- Health Outcomes Research
Background:
- Postoperative morbidity in pediatric cardiac surgery, including intensive care unit (ICU) length of stay and complication counts, presents unique statistical challenges.
- Count data with excess zeros often require specialized models like zero-inflated Poisson regression, yet variable selection methods for these models are limited.
- Identifying key risk factors for pediatric cardiac surgery morbidity is crucial for improving patient outcomes and clinical decision-making.
Purpose of the Study:
- To propose and evaluate a novel statistical approach for predicting postoperative morbidity in children undergoing cardiac surgery.
- To address the methodological gap in variable selection for zero-inflated regression models.
- To identify significant clinical and biomarker risk factors associated with postoperative morbidity in this patient population.
Main Methods:
- Development of regularized zero-inflated Poisson models utilizing a penalized likelihood function.
- Implementation of a new expectation-maximization algorithm for efficient numerical optimization.
- Validation of the proposed method through simulation studies comparing its performance against existing techniques.
Main Results:
- The proposed regularized zero-inflated Poisson model demonstrated superior performance compared to competing methods in simulation studies.
- Application of the developed methods to analyze postoperative morbidity data from a multi-center National Institutes of Health study.
- Improved model fitting and identification of significant clinical and biomarker risk factors for postoperative morbidity.
Conclusions:
- The novel statistical approach offers a robust framework for analyzing count data with excess zeros in pediatric cardiac surgery research.
- The method effectively addresses variable selection challenges in zero-inflated regression models, enhancing risk factor identification.
- This work provides valuable insights into predicting and mitigating postoperative morbidity in pediatric cardiac surgery patients.
Abstract:
This paper proposes a new statistical approach for predicting postoperative morbidity such as intensive care unit length of stay and number of complications after cardiac surgery in children. In a recent multi-center study sponsored by the National Institutes of Health, 311 children undergoing cardiac surgery were enrolled. Morbidity data are count data in which the observations take only nonnegative integer values. Often, the number of zeros in the sample cannot be accommodated properly by a simple model, thus requiring a more complex model such as the zero-inflated Poisson regression model. We are interested in identifying important risk factors for postoperative morbidity among many candidate predictors. There is only limited methodological work on variable selection for the zero-inflated regression models. In this paper, we consider regularized zero-inflated Poisson models through penalized likelihood function and develop a new expectation-maximization algorithm for numerical optimization. Simulation studies show that the proposed method has better performance than some competing methods. Using the proposed methods, we analyzed the postoperative morbidity, which improved the model fitting and identified important clinical and biomarker risk factors.
