本文讨论了事件发生时间结果和假设风险因素的流行病学关联分析中的方法学挑战，其中在某些情况下可能会缺少结果发生时的年龄/时间，这是自我报告结果时常见的情况一项对结果确定进行长期随访的队列研究，例如儿童癌症幸存者研究 (CCSS)，这是一项针对 1970 年至 1999 年诊断的儿童癌症 5 年幸存者的大型队列研究，其中的发病率和年龄调查中自我报告了各种慢性健康状况（CHC）的发作。讨论了处理缺失发病年龄的简单方法及其在暴露-结果关联推断中的潜在偏差。讨论了区间删失方法作为处理该问题的补救措施。通过蒙特卡罗模拟比较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、骨质疏松症/骨质减少和生长激素缺乏症）。区间删失方法在实践中可使用标准统计软件使用。模拟研究表明，与简单方法相比，“区间删失”方法的回归系数估计始终显示出较低的偏差，并且在大多数情况下，标准偏差较小，从而导致较小的均方误差，无论具有感兴趣事件的受试者比例、缺失发病年龄的比例以及样本量。区间删失方法是一种统计上有效且实用的方法，用于对发病年龄时自我报告的事件时间数据进行关联分析可能会丢失。虽然将此类数据强制转换为完整数据的更简单方法可能使标准分析方法适用，但相对于区间删失方法，准确性和精密度都有相当大的损失。版权所有 © 2024。由 Elsevier Inc. 出版。

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 ascer- tainment 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 infer- ence 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 usable in practice using the standard statisti- cal software. The simulation study showed that the regression coefficient estimates from the 'Interval censored' method consistently displayed reduced bias and, in most cases, smaller stan- dard 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.Copyright © 2024. Published by Elsevier Inc.