Abstract：
OBJECTIVE: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
目的：本文讨论了对事件发生时间和假设危险因素进行流行病学关联分析的方法学挑战。在某些情况下，结果开始时的年龄/时间可能会丢失，结果自我报告时通常会遇到的情况。
方法：一项长期随访的队列研究，以确定预后，例如儿童癌症幸存者研究（CCSS），一项针对1970-1999年诊断的5年儿童癌症幸存者的大型队列研究,在调查中自我报告了各种慢性健康状况(CHCs)的发生率和发病年龄.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倚。讨论了间隔删失方法作为解决此问题的一种补救措施。通过蒙特卡罗模拟比较了这些方法的有限样本性能。来自CCSS的例子包括四个CHC(糖尿病，心肌梗塞,骨质疏松/骨质减少,和生长激素缺乏)。
结果：使用标准统计软件在实践中可以使用间隔删失方法。模拟研究表明，“间隔删失”方法的回归系数估计始终显示出降低的偏差，在大多数情况下,较小的标准偏差，导致较小的均方误差，与那些简单的方法相比，不管有感兴趣事件的受试者的比例，缺失发病年龄的比例，和样本量。
结论：当发病年龄可能缺失时，间隔删失方法是一种对自我报告的事件发生时间数据进行关联分析的统计学有效和实用的方法。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能够适用，相对于间隔删失方法，准确性和精密度都有相当大的损失。
方法：一项长期随访的队列研究，以确定预后，例如儿童癌症幸存者研究（CCSS），一项针对1970-1999年诊断的5年儿童癌症幸存者的大型队列研究,在调查中自我报告了各种慢性健康状况(CHCs)的发生率和发病年龄.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倚。讨论了间隔删失方法作为解决此问题的一种补救措施。通过蒙特卡罗模拟比较了这些方法的有限样本性能。来自CCSS的例子包括四个CHC(糖尿病，心肌梗塞,骨质疏松/骨质减少,和生长激素缺乏)。
结果：使用标准统计软件在实践中可以使用间隔删失方法。模拟研究表明，“间隔删失”方法的回归系数估计始终显示出降低的偏差，在大多数情况下,较小的标准偏差，导致较小的均方误差，与那些简单的方法相比，不管有感兴趣事件的受试者的比例，缺失发病年龄的比例，和样本量。
结论：当发病年龄可能缺失时，间隔删失方法是一种对自我报告的事件发生时间数据进行关联分析的统计学有效和实用的方法。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能够适用，相对于间隔删失方法，准确性和精密度都有相当大的损失。
