Wang Zhu, Ma Shuangge, Wang Ching-Yun, Zappitelli Michael, Devarajan Prasad, Parikh Chirag
Department of Research, Connecticut Children's Medical Center, Department of Pediatrics, University of Connecticut School of Medicine, Hartford, CT, U.S.A.
Stat Med. 2014 Dec 20;33(29):5192-208. doi: 10.1002/sim.6314. Epub 2014 Sep 26.
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.
本文提出了一种新的统计方法，用于预测儿童心脏手术后的术后发病率，如重症监护病房住院时间和并发症数量。在最近一项由美国国立卫生研究院赞助的多中心研究中，招募了311名接受心脏手术的儿童。发病率数据是计数数据，其中观察值仅取非负整数值。通常，简单模型无法妥善处理样本中零值的数量，因此需要更复杂的模型，如零膨胀泊松回归模型。我们感兴趣的是在众多候选预测因素中识别术后发病的重要风险因素。关于零膨胀回归模型的变量选择，只有有限的方法学研究。在本文中，我们通过惩罚似然函数考虑正则化零膨胀泊松模型，并开发了一种新的期望最大化算法用于数值优化。模拟研究表明，所提出的方法比一些竞争方法具有更好的性能。使用所提出的方法，我们分析了术后发病率，改善了模型拟合并识别出重要的临床和生物标志物风险因素。
