Research Article Identifying the Prognosis Factors in Death after Liver Transplantation via Adaptive LASSO in Iran Hadi Raeisi Shahraki, Saeedeh Pourahmad, and Seyyed Mohammad Taghi Ayatollahi Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran Correspondence should be addressed to Seyyed Mohammad Taghi Ayatollahi; ayatolahim@sums.ac.ir Received 28 April 2016; Accepted 7 August 2016 Academic Editor: Pam R. Factor-Litvak Copyright © 2016 Hadi Raeisi Shahraki et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Despite the widespread use of liver transplantation as a routine therapy in liver diseases, the efective factors on its outcomes are still controversial. Tis study attempted to identify the most efective factors on death afer liver transplantation. For this purpose, modifed least absolute shrinkage and selection operator (LASSO), called Adaptive LASSO, was utilized. One of the best advantages of this method is considering high number of factors. Terefore, in a historical cohort study from 2008 to 2013, the clinical fndings of 680 patients undergoing liver transplant surgery were considered. Ridge and Adaptive LASSO regression methods were then implemented to identify the most efective factors on death. To compare the performance of these two models, receiver operating characteristic (ROC) curve was used. According to the results, 12 factors in Ridge regression and 9 ones in Adaptive LASSO regression were signifcant. Te area under the ROC curve (AUC) of Adaptive LASSO was equal to 89% (95% CI: 86%–91%), which was signifcantly greater than Ridge regression (64%, 95% CI: 61%–68%) ( < 0.001). As a conclusion, the signifcant factors and the performance criteria revealed the superiority of Adaptive LASSO method as a penalized model versus traditional regression model in the present study. 1. Introduction Liver transplantation is recognized as a well-established therapy for patients with acute liver failure [1–3]. Despite the fact that it has become widespread and recently the number of liver transplants throughout the world has exceeded 15000 cases in a year, the clinical efective risk factors on liver transplantation outcome are still controversial [4]. Logistic regression is the most common method for assessing the efects of various factors on the binary outcome [5]. Usually, in order to avoid modeling bias, at the initial stage of modeling, a high number of variables are candidates [6]. But logistic regression may encounter with multicollinearity problem (strong correlation between two or more than two independent variables in regression models) in modeling the relation among a high number of variables [7, 8]. In these settings, Ridge regression is a traditional remedial method which can control multicollinearity by imposing a slight bias in the estimation of coefcients. Penalized regressions have recently developed models in facing high dimensional data. Imposing a penalty on the coefcients in penalized methods, besides controlling the multicollinearity, represents a sparse and interpretable model [9]. For instance, least absolute shrinkage and selection operator (LASSO), as one of the most famous penalized models, is applicable regardless of the number of variables and sample size [10]. In some of the previous researches in liver disease, penal- ized methods were applied and superiority of them versus conventional statistical methods was confrmed by some authors [11, 12]. On the other hand, although the risk of death and their associated efective factors afer liver transplanta- tion was investigated in some studies, due to the limitations of conventional statistical methods, a few potential factors were considered. Recently, penalized regression had been widely used in medical sciences for modeling and identifying the most important factors. However, artifcial neural networks (ANNs) are frequently used as the nonparametric substituted modeling methods in the issue of large sample size and a high number of variables [2]. Hindawi Publishing Corporation Journal of Environmental and Public Health Volume 2016, Article ID 7620157, 6 pages http://dx.doi.org/10.1155/2016/7620157