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.

分析事件时间数据的最常见数据分析程序之一是危害模型。社会科学和流行病学中的大多数应用都使用横断面调查的数据。这样的调查通常依靠受访者的自传记忆报告。因此，这些数据可能由于记忆力故障而遭受测量错误。由于参数估计值和模型拟合受到影响，因此在实践研究中经常发生这种错误，这是一种危险的做法。尽管有关于线性和非线性回归模型测量误差影响的效果的广泛文献，但记忆误差对危险模型的影响并不广为人知。该项目将使用访谈和管理记录调查流行病学和社会学环境中的日期自传误差的测量误差的类型和数量。使用在回顾性数据上未应用的技术，将开发用于解释内存故障的模型。记忆失败对估计和危险模型的拟合的影响将得到研究。此外，将使用基于经验的基于内存故障的统计模型，研究不同误差校正方法的性能。