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肝癌患者生存模型。

Modelling survival in hepatocellular carcinoma.

机构信息

United BioSource Corporation, Budapest, Hungary.

出版信息

Curr Med Res Opin. 2012 Jul;28(7):1141-53. doi: 10.1185/03007995.2012.691422. Epub 2012 Jun 26.

Abstract

OBJECTIVES

To identify the pattern of the risk of death over long-term in unresectable hepatocellular carcinoma by determining the appropriate distribution to extrapolate overall survival and to assess the role of the Weibull distribution as the standard survival model in oncology.

RESEARCH DESIGN AND METHODS

To select the appropriate distribution, three types of data sources have been analysed. Patient level data from two randomized controlled trials and published Kaplan-Meier curves from a systematic literature review provided short term follow-up data. They were supplemented with patient level data, with long-term follow-up from the Cancer Institute New South Wales, Australia. Published Kaplan-Meier curves were read in and a time-to-event dataset was created. Distributions were fitted to the data from the different sources separately. Their fit was assessed visually and compared using statistical criteria based on log-likelihood, the Akaike information criterion (AIC), and the Bayesian information criterion (BIC).

RESULTS

Based on both published and patient-level, and both short- and long-term follow-up data, the Weibull distribution, used very often in cost-effectiveness models in oncology, does not seem to offer a good fit in hepatocellular carcinoma among the different survival models. The best fitting distribution appears to be the lognormal, with loglogistic as the second-best fitting function. Results were consistent between the different sources of data.

CONCLUSIONS

In unresectable hepatocellular carcinoma, the Weibull model, which is often treated at the gold standard, does not appear to be appropriate based on different sources of data (two clinical trials, a retrospective database and published Kaplan-Meier curves). Lognormal distribution seems to be the most appropriate distribution for extrapolating overall survival.

摘要

目的

通过确定适当的分布来推断总体生存率,从而确定不可切除肝细胞癌的长期死亡风险模式,并评估威布尔分布作为肿瘤学标准生存模型的作用。

研究设计和方法

为了选择合适的分布,分析了三种类型的数据来源。来自两项随机对照试验的患者水平数据和系统评价中发表的 Kaplan-Meier 曲线提供了短期随访数据。它们补充了来自澳大利亚新南威尔士癌症研究所的患者水平数据和长期随访数据。发表的 Kaplan-Meier 曲线被读取,并创建了一个时间事件数据集。分别对来自不同来源的数据进行分布拟合。通过基于对数似然、Akaike 信息准则(AIC)和贝叶斯信息准则(BIC)的统计标准,对拟合情况进行了直观评估和比较。

结果

基于发表的数据和患者水平数据,以及短期和长期随访数据,威布尔分布(在肿瘤学中的成本效益模型中经常使用)似乎不符合不同生存模型中的肝细胞癌的良好拟合。最佳拟合分布似乎是对数正态分布,对数逻辑分布是第二最佳拟合函数。结果在不同的数据来源之间是一致的。

结论

在不可切除的肝细胞癌中,威布尔模型(通常被视为金标准)似乎不适用于不同的数据来源(两项临床试验、一个回顾性数据库和发表的 Kaplan-Meier 曲线)。对数正态分布似乎是推断总体生存率的最合适分布。

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