Liu Yutong, Lin Feng-Chang, Lin Jessica T, Li Quefeng
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A.
Institute of Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A.
J Data Sci. 2022 Jan;20(1):51-78. doi: 10.6339/21-jds1026. Epub 2021 Dec 9.
A standard competing risks set-up requires both time to event and cause of failure to be fully observable for all subjects. However, in application, the cause of failure may not always be observable, thus impeding the risk assessment. In some extreme cases, none of the causes of failure is observable. In the case of a recurrent episode of malaria following treatment, the patient may have suffered a relapse from a previous infection or acquired a new infection from a mosquito bite. In this case, the time to relapse cannot be modeled when a competing risk, a new infection, is present. The efficacy of a treatment for preventing relapse from a previous infection may be underestimated when the true cause of infection cannot be classified. In this paper, we developed a novel method for classifying the latent cause of failure under a competing risks set-up, which uses not only time to event information but also transition likelihoods between covariates at the baseline and at the time of event occurrence. Our classifier shows superior performance under various scenarios in simulation experiments. The method was applied to infection data to classify recurrent infections of malaria.
标准的竞争风险设定要求对所有受试者的事件发生时间和失败原因都能完全观察到。然而，在实际应用中，失败原因可能并非总是可观察到的，从而阻碍了风险评估。在某些极端情况下，没有任何失败原因是可观察到的。在治疗后疟疾复发的情况下，患者可能是先前感染复发，或者是被蚊子叮咬后感染了新的病原体。在这种情况下，当存在竞争风险（新感染）时，复发时间就无法建模。当无法对感染的真正原因进行分类时，预防先前感染复发的治疗效果可能会被低估。在本文中，我们开发了一种在竞争风险设定下对潜在失败原因进行分类的新方法，该方法不仅使用事件发生时间信息，还使用基线和事件发生时协变量之间的转移可能性。我们的分类器在模拟实验的各种场景下都表现出卓越的性能。该方法被应用于感染数据，以对疟疾的复发性感染进行分类。
