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ORIGINAL ARTICLE
Year : 2017  |  Volume : 22  |  Issue : 1  |  Page : 115

Comparing of cox model and parametric models in analysis of effective factors on event time of neuropathy in patients with type 2 diabetes


1 Department of Epidemiology, Faculty of Health, Iran University of Medical Sciences, Tehran, Iran
2 Radiation Biology Research Center, Department of Epidemiology, Faculty of Health, Iran University of Medical Sciences, Tehran, Iran
3 Department of Biostatistics, Faculty of Health, Iran University of Medical Sciences, Tehran, Iran
4 Endocrine Research Center, Firouzgar Hospital; Department of Endocrinology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

Correspondence Address:
Shahnaz Rimaz
Radiation Biology Research Center, Department of Epidemiology, Faculty of Health, Iran University of Medical Sciences, Tehran
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jrms.JRMS_6_17

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Background: 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. Materials and Methods: 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 (P < 0.20) were entered into the multivariate Cox and parametric models (P < 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). Results: 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 (P < 0.05). Conclusion: 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.


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