Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
School of Public Health, University of Alberta, Edmonton, Alberta, Canada.
J Clin Epidemiol. 2024 Sep;173:111458. doi: 10.1016/j.jclinepi.2024.111458. Epub 2024 Jul 9.
This paper discusses methodological challenges in epidemiological association analysis of a time-to-event outcome and hypothesized risk factors, where age/time at the onset of the outcome may be missing in some cases, a condition commonly encountered when the outcome is self-reported.
A cohort study with long-term follow-up for outcome ascertainment such as the Childhood Cancer Survivor Study (CCSS), a large cohort study of 5-year survivors of childhood cancer diagnosed in 1970-1999 in which occurrences and age at onset of various chronic health conditions (CHCs) are self-reported in surveys. Simple methods for handling missing onset age and their potential bias in the exposure-outcome association inference are discussed. The interval-censored method is discussed as a remedy for handling this problem. The finite sample performance of these approaches is compared through Monte Carlo simulations. Examples from the CCSS include four CHCs (diabetes, myocardial infarction, osteoporosis/osteopenia, and growth hormone deficiency).
The interval-censored method is useable in practice using the standard statistical software. The simulation study showed that the regression coefficient estimates from the 'Interval censored' method consistently displayed reduced bias and, in most cases, smaller standard deviations, resulting in smaller mean square errors, compared to those from the simple approaches, regardless of the proportion of subjects with an event of interest, the proportion of missing onset age, and the sample size.
The interval-censored method is a statistically valid and practical approach to the association analysis of self-reported time-to-event data when onset age may be missing. While the simpler approaches that force such data into complete data may enable the standard analytic methods to be applicable, there is considerable loss in both accuracy and precision relative to the interval-censored method.
本文讨论了在时间事件结局和假设风险因素的流行病学关联分析中出现的方法学挑战，在某些情况下，结局可能会缺失，这种情况在结局是自我报告时经常出现。
本研究为队列研究，对结局进行长期随访以确定，例如儿童癌症幸存者研究（CCSS），这是一项针对 1970-1999 年诊断的儿童癌症 5 年幸存者的大型队列研究，通过调查自我报告了各种慢性健康状况（CHC）的发生和发病年龄。讨论了处理缺失发病年龄的简单方法及其对暴露-结局关联推断的潜在偏差。讨论了间隔 censored 方法作为处理此问题的一种补救方法。通过蒙特卡罗模拟比较了这些方法的有限样本性能。CCSS 的示例包括四种 CHC（糖尿病、心肌梗死、骨质疏松/骨量减少和生长激素缺乏症）。
标准统计软件可用于实际应用中的间隔 censored 方法。模拟研究表明，与简单方法相比，“间隔 censored”方法得出的回归系数估计值始终显示出较小的偏差，在大多数情况下，标准偏差较小，均方误差较小，无论感兴趣事件的受试者比例、缺失发病年龄的比例和样本量如何。
当发病年龄可能缺失时，间隔 censored 方法是一种用于自我报告时间事件数据关联分析的有效且实用的统计学方法。虽然将此类数据强制为完整数据的更简单方法可以使标准分析方法适用，但与间隔 censored 方法相比，准确性和精度都有很大损失。
