ASSESSING NEW MATHEMATICAL MODELS FOR MEDICAL EVENTS

为医疗结果生成估计概率的预测模型已被广泛应用于卫生服务研究、卫生政策以及越来越多地用于卫生保健评估和实时决策支持。医疗事件的Logistic回归模型是大多数概率预测性临床决策辅助工具的核心，也是基于风险调整事件的医疗保健比较分析的基础。在这种应用中，对患者风险的不准确评估可能会对卫生保健和卫生政策产生重大影响。新的基于计算机的建模技术，包括广义加性模型、分类树和神经网络，可能潜在地捕获回归方法可能遗漏或错误描述的信息。然而，这些方法在模型构建中使用非常局部的信息，并且可能过度适合样本数据，因此不能很好地传输到新的设置。在1-3年中，我们调查了这些建模方法在各种数据结构下做出的预测的相对准确性，包括异常值和缺失数据的存在。对于这些数据结构中的许多，我们发现更“本地”的过程经常不能像传统的回归方法那样适用于新的测试数据。然而，我们的结果表明，随着样本大小和数据复杂性的增加，这些过程的性能可能会显著提高。因此，为了在更一般的条件下检验这些发现，我们现在建议再进行两年的研究：1)严格评估其他新的创新建模方法和原始混合模型构建方法的相对预测性能和可移植性；2)系统地调查应用于大型和复杂数据结构的建模方法的相对预测性能和模型可移植性；以及3)探索和评估分类树和神经网络的离群点和缺失数据处理程序。拟议工作的完成将导致首次系统地探索影响用于预测医疗结果的主要建模方法的预测性能的因素，以及由这些方法构建的模型在研究人员越来越可用的这类超大数据集上的比较性能。

Predictive models that generate estimate probabilities for medical outcomes have become widely used in health services research, in health policy, and increasingly, for the assessment of health care and for real-time decision support. Logistic regression models for medical events are central to most probabilistic predictive clinical decision aids and are fundamental to comparative analyses of medical care based on risk-adjusted events. In such applications, inaccurate assessment of patient risk can have significant health care and health policy implications. New computer-based modeling techniques including generalized additive models, classification trees, and neural networks may potentially capture information that regression methods may miss or misrepresent. However, these methods use very local information in model construction and may be overfit to the sample data and thus not transport well to new settings. In years 1-3, we investigated the relative accuracy of predictions made by these modeling methods under a variety of data structures, including the presence of outliers and missing data. For many of these data structures we found that the more "local" procedures frequently did not generalize to new test data as well as traditional regression methods. However, our results suggest that as sample size and data complexity increases the performance of these procedures may substantially improved. Thus, to test these findings under more general conditions, we now propose two additional years of research to 1) rigorously assess the relative predictive performance and transportability of other new innovative modeling methods and of original hybrid model construction methods; 2) systematically investigate the relative predictive performance and model transportability of modeling methods applied to large and complex data structures; and 3) explore and assess procedures for handling outliers and missing data for classification trees and neural networks. The completion of the proposed work will result in the first systematic exploration of the factors affecting the predictive performance of the major modeling methods used to predict medical outcomes, and the comparative performance of models constructed by these methods on the extremely large data sets of the type that are becoming increasing available to researchers.