Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran.
Singapore Med J. 2012 May;53(5):336-43.
Oesophageal cancer is one of the most common causes of cancer mortality in developing countries, including Iran. This study aimed to assess factors affecting survival of patients with oesophageal cancer using parametric analysis with frailty models.
Data on 359 patients with oesophageal cancer was collected from the Babol Cancer Registry for the period 1990-1991. By 2006, the patients had been followed up for a period of 15 years. Hazard ratio was used to interpret the risk of death. To explore factors affecting the survival of patients, log-normal and log-logistic models with frailty were examined. The Akaike Information Criterion (AIC) was used for selecting the best model(s). Cox regression was not suitable for this patient group, as the proportionality assumption of the Cox model was not satisfied by our data (p = 0.007).
Multivariate analysis according to parametric models showed that family history of cancer might increase the risk of death from cancer significantly. Based on AIC scores, the log-logistic model with inverse Gaussian frailty seemed more appropriate for our data set, and we propose that the model might prove to be a useful statistical model for the survival analysis of patients with oesophageal cancer. The results suggested that gender and family history of cancer were significant predictors of death from cancer.
Early preventative care for patients with a family history of cancer may be important to decrease the risk of death in patients with oesophageal cancer. Male gender may be associated with a lower risk of death.
食管癌是发展中国家(包括伊朗)癌症死亡的主要原因之一。本研究旨在采用带有脆弱性模型的参数分析来评估影响食管癌患者生存的因素。
从 1990 年至 1991 年期间,我们从巴博勒癌症登记处收集了 359 名食管癌患者的数据。截至 2006 年,这些患者已经随访了 15 年。风险比用于解释死亡风险。为了探讨影响患者生存的因素,我们检查了带有脆弱性的对数正态和对数逻辑模型。Akaike 信息准则(AIC)用于选择最佳模型。由于 Cox 模型的比例假设不满足我们的数据(p=0.007),因此 Cox 回归不适用于该患者群体。
根据参数模型的多变量分析表明,癌症家族史可能会显著增加癌症死亡的风险。根据 AIC 评分,带有逆高斯脆弱性的对数逻辑模型似乎更适合我们的数据集,我们提出该模型可能被证明是食管癌患者生存分析的有用统计模型。结果表明,性别和癌症家族史是癌症死亡的重要预测因子。
对有癌症家族史的患者进行早期预防护理可能对降低食管癌患者的死亡风险很重要。男性可能与较低的死亡风险相关。