Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Am J Epidemiol. 2018 Mar 1;187(3):623-632. doi: 10.1093/aje/kwx281.
Loss to follow-up is an endemic feature of time-to-event analyses that precludes observation of the event of interest. To our knowledge, in typical cohort studies with encounters occurring at regular or irregular intervals, there is no consensus on how to handle person-time between participants' last study encounter and the point at which they meet a definition of loss to follow-up. We demonstrate, using simulation and an example, that when the event of interest is captured outside of a study encounter (e.g., in a registry), person-time should be censored when the study-defined criterion for loss to follow-up is met (e.g., 1 year after last encounter), rather than at the last study encounter. Conversely, when the event of interest must be measured within the context of a study encounter (e.g., a biomarker value), person-time should be censored at the last study encounter. An inappropriate censoring scheme has the potential to result in substantial bias that may not be easily corrected.
失访是事件时间分析中存在的一个特有问题，会妨碍对感兴趣事件的观察。据我们所知，在典型的队列研究中，随访是在定期或不定期的时间间隔内进行的，对于如何处理参与者最后一次研究随访和达到失访定义之间的人员时间，目前尚无共识。我们通过模拟和示例进行了演示，当感兴趣的事件发生在研究随访之外（例如，在注册中心）时，当达到研究定义的失访标准（例如，最后一次随访后 1 年）时，应截除人员时间，而不是在最后一次研究随访时。相反，当感兴趣的事件必须在研究随访的背景下进行测量（例如，生物标志物值）时，应在最后一次研究随访时截除人员时间。不适当的截除方案可能会导致严重的偏差，且这种偏差可能不容易纠正。
