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Accounting for heaping in retrospectively reported event data – a mixture-model approach

Authors


Haim Y. Bar, Department of Statistics, Cornell University, Ithaca, NY, U.S.A.

E-mail: hyb2@cornell.edu

Abstract

When event data are retrospectively reported, more temporally distal events tend to get ‘heaped’ on even multiples of reporting units. Heaping may introduce a type of attenuation bias because it causes researchers to mismatch time-varying right-hand side variables. We develop a model-based approach to estimate the extent of heaping in the data and how it affects regression parameter estimates. We use smoking cessation data as a motivating example, but our method is general. It facilitates the use of retrospective data from the multitude of cross-sectional and longitudinal studies worldwide that collect and potentially could collect event data. Copyright © 2012 John Wiley & Sons, Ltd.

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