Wolbers Marcel, Koller Michael T, Witteman Jacqueline C M, Steyerberg Ewout W
Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Basel, Switzerland.
Epidemiology. 2009 Jul;20(4):555-61. doi: 10.1097/EDE.0b013e3181a39056.
Clinical decision-making often relies on a subject's absolute risk of a disease event of interest. However, in a frail population, competing risk events may preclude the occurrence of the event of interest. We review competing-risk regression models with a view toward predictive modeling. We show how measures of prognostic performance (such as calibration and discrimination) can be adapted to the competing-risks setting. An example of coronary heart disease (CHD) prediction in women aged 55-90 years in the Rotterdam study is used to illustrate the proposed methods, and to compare the Fine and Gray regression model to 2 alternative approaches: (1) a standard Cox survival model, which ignores the competing risk of non-CHD death, and (2) a cause-specific hazards model, which combines proportional hazards models for the event of interest and the competing event. The Fine and Gray model and the cause-specific hazards model perform similarly. However, the standard Cox model substantially overestimates 10-year risk of CHD; it classifies 18% of the individuals as high risk (>20%), compared with only 8% according to the Fine and Gray model. We conclude that competing risks have to be considered explicitly in frail populations such as the elderly.
临床决策通常依赖于个体发生感兴趣疾病事件的绝对风险。然而，在体弱人群中，竞争风险事件可能会阻止感兴趣事件的发生。我们以预测建模为目的回顾竞争风险回归模型。我们展示了预后性能指标（如校准和区分度）如何适用于竞争风险环境。在鹿特丹研究中，以55至90岁女性冠心病（CHD）预测为例来说明所提出的方法，并将费恩和格雷回归模型与两种替代方法进行比较：（1）标准的考克斯生存模型，该模型忽略了非冠心病死亡的竞争风险；（2）特定病因风险模型，该模型结合了感兴趣事件和竞争事件的比例风险模型。费恩和格雷模型与特定病因风险模型表现相似。然而，标准的考克斯模型大幅高估了冠心病的10年风险；它将18%的个体归类为高风险（>20%），而根据费恩和格雷模型这一比例仅为8%。我们得出结论，在老年人等体弱人群中必须明确考虑竞争风险。
