Medicine, University of Ottawa; Epidemiology and Community Medicine, University of Ottawa; Ottawa Hospital Research Institute; ICES uOttawa.
Ottawa Hospital Research Institute.
J Clin Epidemiol. 2016 Feb;70:101-5. doi: 10.1016/j.jclinepi.2015.08.010. Epub 2015 Sep 26.
Kaplan-Meier (KM) analyses are frequently used to measure outcome risk over time. These analyses overestimate risk whenever competing events are present. Many published KM analyses are susceptible to such competing risk bias. This study derived and validated a model that predicted true outcome risk based on the biased KM risk.
We simulated survival data sets having a broad range of 1-year true outcome and competing event risk. Unbiased true outcome risk estimates were calculated using the cumulative incidence function (CIF). Multiple linear regression was used to determine the independent association of CIF-based true outcome risk with the biased KM risk and the proportion of all outcomes that were competing events.
The final model found that both the biased KM-based risk and the proportion of all outcomes that were competing events were strongly associated with CIF-based risk. In validation populations that used a variety of distinct survival hazard functions, the model accurately predicted the CIF (R(2) = 1).
True outcome risk can be accurately predicted from KM estimates susceptible to competing risk bias.
卡普兰-迈耶（KM）分析常用于随时间测量结果风险。只要存在竞争事件，这些分析就会高估风险。许多已发表的 KM 分析都容易受到这种竞争风险偏差的影响。本研究基于有偏的 KM 风险，推导出并验证了一种预测真实结果风险的模型。
我们模拟了具有广泛的 1 年真实结果和竞争事件风险的生存数据集。使用累积发生率函数（CIF）计算无偏的真实结果风险估计值。多元线性回归用于确定 CIF 为基础的真实结果风险与有偏的 KM 风险以及所有竞争事件结果的比例之间的独立关联。
最终模型发现，有偏的 KM 风险和所有竞争事件结果的比例均与 CIF 风险密切相关。在使用各种不同生存危险函数的验证人群中，该模型准确地预测了 CIF（R²=1）。
可以从易受竞争风险偏差影响的 KM 估计中准确预测真实结果风险。
