Department of Breast Surgery, General surgery, the First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
World J Gastroenterol. 2011 Jun 21;17(23):2867-72. doi: 10.3748/wjg.v17.i23.2867.
To investigate the efficiency of Cox proportional hazard model in detecting prognostic factors for gastric cancer.
We used the log-normal regression model to evaluate prognostic factors in gastric cancer and compared it with the Cox model. Three thousand and eighteen gastric cancer patients who received a gastrectomy between 1980 and 2004 were retrospectively evaluated. Clinic-pathological factors were included in a log-normal model as well as Cox model. The akaike information criterion (AIC) was employed to compare the efficiency of both models. Univariate analysis indicated that age at diagnosis, past history, cancer location, distant metastasis status, surgical curative degree, combined other organ resection, Borrmann type, Lauren's classification, pT stage, total dissected nodes and pN stage were prognostic factors in both log-normal and Cox models.
In the final multivariate model, age at diagnosis, past history, surgical curative degree, Borrmann type, Lauren's classification, pT stage, and pN stage were significant prognostic factors in both log-normal and Cox models. However, cancer location, distant metastasis status, and histology types were found to be significant prognostic factors in log-normal results alone. According to AIC, the log-normal model performed better than the Cox proportional hazard model (AIC value: 2534.72 vs 1693.56).
It is suggested that the log-normal regression model can be a useful statistical model to evaluate prognostic factors instead of the Cox proportional hazard model.
探讨 Cox 比例风险模型在检测胃癌预后因素方面的效率。
我们使用对数正态回归模型来评估胃癌的预后因素，并将其与 Cox 模型进行比较。回顾性评估了 1980 年至 2004 年间接受胃切除术的 3118 例胃癌患者。对数正态模型和 Cox 模型中均包含临床病理因素。采用赤池信息量准则（AIC）比较两种模型的效率。单因素分析表明，诊断时的年龄、既往史、肿瘤位置、远处转移状态、手术根治程度、联合其他器官切除、Borrman 分型、Lauren 分类、pT 分期、总解剖淋巴结数和 pN 分期是对数正态和 Cox 模型中的预后因素。
在最终的多变量模型中，诊断时的年龄、既往史、手术根治程度、Borrman 分型、Lauren 分类、pT 分期和 pN 分期是对数正态和 Cox 模型中的显著预后因素。然而，肿瘤位置、远处转移状态和组织学类型仅在对数正态结果中是显著的预后因素。根据 AIC，对数正态模型的表现优于 Cox 比例风险模型（AIC 值：2534.72 与 1693.56）。
建议对数正态回归模型可以作为评估预后因素的有用统计模型，而不是 Cox 比例风险模型。
