Accounting for heaping in retrospectively reported event data - a mixture-model approach.
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.
Department of Statistics, Cornell University, Ithaca, NY, USA. hyb2@cornell.edu
Haim Y
HY
Bar
Dean R
DR
Lillard
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D012189:Retrospective Studies; D016540:Smoking Cessation / Q000379:methods; D013269:Stochastic Processes