...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>BMC Medical Informatics and Decision Making >Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers
【24h】

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

机译：扩展决策曲线分析，一种评估诊断测试，预测模型和分子标记的新方法

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. Methods In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Results Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Conclusion Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.
机译：背景决策曲线分析是一种用于评估诊断测试，预测模型和分子标记的新颖方法。它结合了准确度测量的数学简便性（例如敏感性和特异性）以及决策分析方法的临床适用性。最关键的是，决策曲线分析可以直接应用于数据集，不需要传统决策分析技术通常需要的有关成本，收益和偏好的外部数据。方法在本文中，我们介绍了决策曲线分析的一些扩展，包括对过度拟合的校正，置信区间，对检查数据（包括竞争风险）的应用以及直接根据预测概率计算决策曲线。所有这些扩展都是基于先前在文献中已经描述过的直接应用于类似统计技术的方法。结果仿真研究表明，重复的10倍交叉验证是校正过拟合决策曲线的最佳方法。将决策曲线应用于审查数据的方法几乎没有偏差，覆盖范围极佳;对于竞争风险，决策曲线受竞争风险的发生率以及竞争风险与兴趣预测因子之间的关联性的影响。直接从预测的概率计算决策曲线导致了决策曲线的平滑。结论决策曲线分析可以轻松地扩展到预测模型性能度量常用的许多应用程序。提供了用于执行决策曲线分析的软件。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号