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

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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.