De Laurentiis M, Ravdin P M
Department of Medicine, University of Texas Health Science Center, San Antonio 78284.
Cancer Lett. 1994 Mar 15;77(2-3):127-38. doi: 10.1016/0304-3835(94)90095-7.
The purpose of this study was to demonstrate how a form of neural network analysis could be used to perform survival analysis on censored data, and to compare neural network analysis with the most commonly used technique for this type of analysis, Cox regression. In this study computer simulated data sets were used. The underlying rules connecting prognostic information to the hazard of death were defined to allow the construction of data sets with specific realistic properties that could be used to demonstrate situations in which neural network analysis had particular strengths in comparison with Cox regression modeling. Using these simulated data sets neural network analysis could produce successful predictive models, find interactions between variables, and recognize the importance of variables that contributed to the hazard rate as a complex function of the variables value and in situations where the proportionality of hazards assumption was violated. It was also demonstrated that neural network analysis was not a 'black box', but could lead to useful insights into the roles played by different prognostic variables in determining patient outcome.
本研究的目的是演示如何使用一种神经网络分析形式对删失数据进行生存分析，并将神经网络分析与这类分析中最常用的技术——Cox回归进行比较。在本研究中，使用了计算机模拟数据集。定义了将预后信息与死亡风险联系起来的潜在规则，以便构建具有特定现实属性的数据集，这些数据集可用于演示与Cox回归建模相比，神经网络分析具有独特优势的情形。使用这些模拟数据集，神经网络分析能够生成成功的预测模型，找到变量之间的相互作用，并识别作为变量值复杂函数且在危险比例假设被违反的情况下对危险率有贡献的变量的重要性。研究还表明，神经网络分析并非一个“黑匣子”，而是能够对不同预后变量在确定患者预后中所起的作用提供有益的见解。
