Log-normal censored regression model detecting prognostic factors in gastric cancer:A study of 3018 cases
查看参考文献15篇
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文摘
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AIM:To investigate the efficiency of Cox proportional hazard model in detecting prognostic factors for gastric cancer.METHODS: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.RESULTS: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).CONCLUSION: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. |
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来源
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World Journal of Gastroenterology
,2011,17(23):2867-2872 【核心库】
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关键词
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Gastric cancer
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Log normal regression model
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Cox proportional hazard model
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Prognostic factors
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地址
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Department of Breast Surgery,General surgery,the First Hospital of China Medical University, Liaoning, Shenyang, 110001
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语种
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英文 |
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文献类型
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研究性论文 |
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ISSN
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1007-9327 |
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学科
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外科学 |
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基金
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Gastric Cancer Laboratory and Pathology Department of Chinese Medical University,Shenyang,China
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Science and Technology Program of Shenyang
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文献收藏号
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CSCD:4251556
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