Kargarian-Marvasti Sadegh, Rimaz Shahnaz, Abolghasemi Jamileh, Heydari Iraj
Department of Epidemiology, Faculty of Health, Iran University of Medical Sciences, Tehran, Iran.
Radiation Biology Research Center, Department of Epidemiology, Faculty of Health, Iran University of Medical Sciences, Tehran, Iran.
J Res Med Sci. 2017 Oct 31;22:115. doi: 10.4103/jrms.JRMS_6_17. eCollection 2017.
Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting the event time of neuropathy in patients with type 2 diabetes.
This study included 371 patients with type 2 diabetes without neuropathy who were registered at Fereydunshahr diabetes clinic. Subjects were followed up for the development of neuropathy between 2006 to March 2016. To investigate the factors influencing the event time of neuropathy, significant variables in univariate model ( < 0.20) were entered into the multivariate Cox and parametric models ( < 0.05). In addition, Akaike information criterion (AIC) and area under ROC curves were used to evaluate the relative goodness of fitted model and the efficiency of each procedure, respectively. Statistical computing was performed using R software version 3.2.3 (UNIX platforms, Windows and MacOS).
Using Kaplan-Meier, survival time of neuropathy was computed 76.6 ± 5 months after initial diagnosis of diabetes. After multivariate analysis of Cox and parametric models, ethnicity, high-density lipoprotein and family history of diabetes were identified as predictors of event time of neuropathy ( < 0.05).
According to AIC, "log-normal" model with the lowest Akaike's was the best-fitted model among Cox and parametric models. According to the results of comparison of survival receiver operating characteristics curves, log-normal model was considered as the most efficient and fitted model.
Cox比例风险模型是分析多个变量对生存时间影响的最常用方法。然而，在某些情况下，参数模型在分析生存数据时比Cox模型能给出更精确的估计。本研究的目的是在2型糖尿病患者神经病变事件时间影响因素的生存分析中，研究Cox模型和参数模型的比较性能。
本研究纳入了371例在费雷敦沙赫尔糖尿病诊所登记的无神经病变的2型糖尿病患者。对研究对象在2006年至2016年3月期间进行随访，观察神经病变的发生情况。为了研究影响神经病变事件时间的因素，将单变量模型中具有显著性的变量（<0.20）纳入多变量Cox模型和参数模型（<0.05）。此外，分别使用赤池信息准则（AIC）和ROC曲线下面积来评估拟合模型的相对优度和每个程序的效率。使用R软件版本3.2.3（UNIX平台、Windows和MacOS）进行统计计算。
使用Kaplan-Meier法计算得出，糖尿病初诊后神经病变的生存时间为76.6±5个月。经过Cox模型和参数模型的多变量分析，种族、高密度脂蛋白和糖尿病家族史被确定为神经病变事件时间的预测因素（<0.05）。
根据AIC，在Cox模型和参数模型中，Akaike值最低的“对数正态”模型是拟合效果最好的模型。根据生存接收者操作特征曲线的比较结果，对数正态模型被认为是效率最高且拟合效果最好的模型。
