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


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



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.