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Accounting for heaping in retrospectively reported event data - a mixture-model approach
1Department of Statistics, Cornell University, Ithaca, NY, USA. hyb2@cornell.edu
Statistics in Medicine
|June 27, 2012
Summary
Retrospective event data often show heaping, where events cluster on reporting dates. This study introduces a method to quantify heaping bias and correct regression estimates, improving analysis of time-varying data.
Area of Science:
- Biostatistics
- Epidemiology
- Health Services Research
Background:
- Retrospective event data collection can lead to 'heaping,' where events cluster on reporting time units.
- This heaping can cause attenuation bias by mismatching time-varying variables in analyses.
Purpose of the Study:
- To develop and present a model-based approach to quantify data heaping.
- To assess the impact of heaping on regression parameter estimates.
- To provide a method for more accurate analysis of retrospective event data.
Main Methods:
- A novel model-based statistical approach is proposed.
- The method estimates the extent of heaping in retrospective event data.
- The impact of heaping on regression coefficients is evaluated.
Main Results:
- The developed method quantifies the degree of heaping in retrospective data.
- The approach demonstrates how heaping affects regression parameter estimates, potentially introducing bias.
- The smoking cessation example illustrates the method's application and utility.
Conclusions:
- The proposed method effectively estimates data heaping and its effect on statistical models.
- This approach enhances the usability of retrospective data from various studies.
- Researchers can better account for reporting biases in longitudinal and cross-sectional studies.
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