Errors in autobiographical memory and their effects in time-to-event analysis

自传体记忆中的错误及其对事件时间分析的影响

• 批准号: 45626069
• 负责人: Professorin Dr. Iris Pigeot
• 金额: --
• 依托单位: Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS GmbH
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2011-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/45626069?language=en
• 关键词: Errors; autobiographical; memory; their; effects

One of the most common data analysis procedures for analyzing time-to-event data are hazard models. Most applications in the social sciences and in epidemiology use data from crosssectional surveys. Such surveys usually rely on respondents' reports based on their autobiographical memory. Therefore, these data may suffer from measurement errors due to memory failure. Ignoring such error, as often happens in practical research, is a dangerous practice, since parameter estimates and model fit are affected. While there is an extensive literature on the effects of measurement error in linear and nonlinear regression models, the impact of memory errors on hazard models in general is not widely known.This project will investigate type and amount of measurement error in autobiographical recall of dates in an epidemiological and sociological context using interview and administrative records. Models for the explanation of memory failures will be developed, using a technique which was not applied before on retrospective data. The effect of memory failures on estimates and fit of hazard models will be investigated. Further, using the empirically based, statistical model for memory failures, the performance of different error correction methods will be studied.

用于分析事件发生时间数据的最常见的数据分析程序之一是风险模型。社会科学和流行病学中的大多数应用都使用来自横断面调查的数据。这类调查通常依赖于受访者基于自传体记忆的报告。因此，这些数据可能由于存储器故障而遭受测量误差。在实际研究中经常会出现这样的错误，忽略这种错误是一种危险的做法，因为参数估计和模型拟合都会受到影响。虽然有大量的文献中的测量误差的影响，在线性和非线性回归模型，记忆错误的危害模型的影响一般是不广为人知的。本项目将调查类型和测量误差的量自传回忆的日期在流行病学和社会学背景下，使用访谈和行政记录。将开发用于解释记忆故障的模型，使用以前未应用于回顾性数据的技术。记忆故障的估计和拟合的危险模型的影响将进行调查。此外，使用经验为基础的，统计模型的内存故障，不同的纠错方法的性能进行了研究。