Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, PO Box 85500, 3508 GA Utrecht, The Netherlands.
J Clin Epidemiol. 2012 Sep;65(9):946-53. doi: 10.1016/j.jclinepi.2012.01.021. Epub 2012 May 31.
The pros and cons of composite end points in prognostic research are discussed, and an adaptation method, designed to accurately adjust absolute risks for a composite end point to risks for the individual component outcomes, is presented.
An example prediction model for recurrent cardiovascular events (composite end point) was used to evaluate the performance regarding the individual component outcomes (cardiovascular death, myocardial infarction, and stroke) before and after the adaptation method.
Discrimination for the individual component outcomes (concordance index for myocardial infarction, 0.68; concordance index for stroke, 0.70) was very similar to discrimination for the original composite end point (concordance index, 0.70). For cardiovascular death, it even increased substantially (concordance index, 0.78). After adaptation, calibration plots for the component outcomes also improved, with visible convergence of the predicted risks and the observed incidences.
In sum, these findings show that the adaptation method is useful when validating or applying a composite end point prediction model to the individual component outcomes. Following from this, recommendations concerning reporting of composite end points in future research are also included. Without the need for extra data, composite end point prediction models can easily be directly expanded to allow for the estimation of risk for each individual component outcome, improving the interpretability for clinicians and patients.
讨论预后研究中复合终点的优缺点，并提出一种适应方法，旨在准确调整复合终点的绝对风险，以适应各个组成部分结局的风险。
使用预测复发性心血管事件（复合终点）的示例预测模型，在适应方法之前和之后评估各个组成部分结局（心血管死亡、心肌梗死和中风）的性能。
各个组成部分结局的区分度（心肌梗死的一致性指数为 0.68；中风的一致性指数为 0.70）与原始复合终点的区分度非常相似（一致性指数为 0.70）。对于心血管死亡，甚至有显著提高（一致性指数为 0.78）。适应后，各个组成部分的校准图也得到了改善，预测风险和观察到的发生率明显趋同。
总之，这些发现表明，当验证或将复合终点预测模型应用于各个组成部分结局时，适应方法非常有用。由此，也包括了对未来研究中报告复合终点的建议。无需额外的数据，复合终点预测模型可以轻松扩展，以估计每个个体组成部分结局的风险，提高临床医生和患者的可解释性。
