肺癌风险预测模型:观点和传播
目的:目的是通过对方法,透明度和验证的严格评估来系统地评估肺癌风险预测模型,从而为将来的模型开发提供指导。 方法:电子搜索(包括PubMed,EMbase,Cochrane图书馆,Web of Science,中国国家知识基础设施,万方,中国生物医学文献数据库以及其他官方癌症网站)的英文和截至2018年4月30日的中文数据库。主要报道来源为输入数据,假设和敏感性分析。模型验证基于出版物中有关内部验证,外部验证和/或交叉验证的声明。 结果:包括22个研究(包含11个多用途和11个单次使用模型)。原始模型在2003年至2016年之间开发。其中大多数来自美国。多元逻辑回归被广泛用于识别模型。每个模型的曲线下最小面积为0.57,最大为0.87。最小的C统计量为0.59,最大的为0.85。六项研究通过外部验证进行了验证,三项进行了交叉验证。总共有2个模型有偏见的高风险,其中6个模型报告了最常用的变量是年龄和吸烟时间,还有5个模型包括肺癌家族史。 结论:该模型的预测准确性总体较高,表明将模型用于高风险人群预测是可行的。但是,模型开发和报告的过程并不是最佳选择,存在很大的偏差风险。此风险影响预测准确性,影响模型的推广和进一步开发。有鉴于此,模型开发人员需要在模型开发中更加专心于偏向风险控制和有效性验证。**
Risk prediction models for lung cancer: Perspectives and dissemination
Objective: The objective was to systematically assess lung cancer risk prediction models by critical evaluation of methodology, transparency and validation in order to provide a direction for future model development. Methods: Electronic searches (including PubMed, EMbase, the Cochrane Library, Web of Science, the China National Knowledge Infrastructure, Wanfang, the Chinese BioMedical Literature Database, and other official cancer websites) were completed with English and Chinese databases until April 30th, 2018. Main reported sources were input data, assumptions and sensitivity analysis. Model validation was based on statements in the publications regarding internal validation, external validation and/or cross-validation. Results: Twenty-two studies (containing 11 multiple-use and 11 single-use models) were included. Original models were developed between 2003 and 2016. Most of these were from the United States. Multivariate logistic regression was widely used to identify a model. The minimum area under the curve for each model was 0.57 and the largest was 0.87. The smallest C statistic was 0.59 and the largest 0.85. Six studies were validated by external validation and three were cross-validated. In total, 2 models had a high risk of bias, 6 models reported the most used variables were age and smoking duration, and 5 models included family history of lung cancer. Conclusions: The prediction accuracy of the models was high overall, indicating that it is feasible to use models for high-risk population prediction. However, the process of model development and reporting is not optimal with a high risk of bias. This risk affects prediction accuracy, influencing the promotion and further development of the model. In view of this, model developers need to be more attentive to bias risk control and validity verification in the development of models.
pmid: 31156302 Chin J Cancer Res 影响因子: 0.0 发表日期: 20190401 官网 免费下载
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