1 Stanford University, Stanford, USA.
2 Johns Hopkins University, Baltimore, USA.
Stat Methods Med Res. 2018 Sep;27(9):2674-2693. doi: 10.1177/0962280216684163. Epub 2017 Jan 8.
In establishing prognostic models, often aided by machine learning methods, much effort is concentrated in identifying good predictors. However, the same level of rigor is often absent in improving the outcome side of the models. In this study, we focus on this rather neglected aspect of model development. We are particularly interested in the use of longitudinal information as a way of improving the outcome side of prognostic models. This involves optimally characterizing individuals' outcome status, classifying them, and validating the formulated prediction targets. None of these tasks are straightforward, which may explain why longitudinal prediction targets are not commonly used in practice despite their compelling benefits. As a way of improving this situation, we explore the joint use of empirical model fitting, clinical insights, and cross-validation based on how well formulated targets are predicted by clinically relevant baseline characteristics (antecedent validators). The idea here is that all these methods are imperfect but can be used together to triangulate valid prediction targets. The proposed approach is illustrated using data from the longitudinal assessment of manic symptoms study.
在建立预后模型时，通常借助机器学习方法，大量精力集中在识别良好的预测因子上。然而，模型的结果方面通常缺乏同样的严谨性。在这项研究中，我们关注模型开发中这个相当被忽视的方面。我们特别感兴趣的是将纵向信息用作改善预后模型结果方面的一种方法。这涉及到对个体的结果状态进行最佳描述，对他们进行分类，并验证所制定的预测目标。这些任务都不简单，这也许可以解释为什么尽管纵向预测目标具有很大的优势，但在实践中却不常用。为了改善这种情况，我们探索了联合使用经验模型拟合、临床见解和基于临床相关基线特征（先行验证器）对所制定目标的预测程度的交叉验证。其思想是，所有这些方法都不完美，但可以结合使用，以三角测量有效的预测目标。该方法使用来自纵向评估躁狂症状研究的数据进行说明。
