TY - JOUR ID - 57627 TI - Comparison of Survival Models for Analyzing Prognostic Factors in Gastric Cancer Patients JO - Asian Pacific Journal of Cancer Prevention JA - APJCP LA - en SN - 1513-7368 AU - Habibi, Danial AU - Rafiei, Mohammad AU - Chehrei, Ali AU - Shayan, Zahra AU - Tafagodi, Soheil AD - Department of Biostatistics, Faculty of Medicine, Arak University of Medical Sciences, Arak,Iran. AD - Pars Clinicopathology Clinicopathology Laboratory, Arak, Iran. AD - Trauma Research Center, Department of Community Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. Y1 - 2018 PY - 2018 VL - 19 IS - 3 SP - 749 EP - 753 KW - Cox regression KW - Parametric models KW - AIC KW - Gastric cancer DO - 10.22034/APJCP.2018.19.3.749 N2 - Objective: There are a number of models for determining risk factors for survival of patients with gastric cancer.This study was conducted to select the model showing the best fit with available data. Methods: Cox regression andparametric models (Exponential, Weibull, Gompertz, Log normal, Log logistic and Generalized Gamma) were utilized inunadjusted and adjusted forms to detect factors influencing mortality of patients. Comparisons were made with AkaikeInformation Criterion (AIC) by using STATA 13 and R 3.1.3 softwares. Results: The results of this study indicated thatall parametric models outperform the Cox regression model. The Log normal, Log logistic and Generalized Gammaprovided the best performance in terms of AIC values (179.2, 179.4 and 181.1, respectively). On unadjusted analysis,the results of the Cox regression and parametric models indicated stage, grade, largest diameter of metastatic nest,largest diameter of LM, number of involved lymph nodes and the largest ratio of metastatic nests to lymph nodes,to be variables influencing the survival of patients with gastric cancer. On adjusted analysis, according to the best model(log normal), grade was found as the significant variable. Conclusion: The results suggested that all parametric modelsoutperform the Cox model. The log normal model provides the best fit and is a good substitute for Cox regression. UR - https://journal.waocp.org/article_57627.html L1 - https://journal.waocp.org/article_57627_f9bc55de7eecc12c33904c795c73d9e4.pdf ER -