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Background: Culpability studies, a common study design in the cannabis crash risk literature, typically report odds-ratios (OR) indicating the raised risks of a culpable accident. This parameter is of unclear policy relevance, and is frequently misinterpreted as an estimate of the increased crash risk, a practice that introduces a substantial “interpretational bias”.

Methods: A Bayesian statistical model for culpability study counts is developed to provide inference for both culpable and total crash risks, with a hierarchical effect specification to allow for meta-analysis across studies with potentially heterogeneous risk parameter values. The model is assessed in a bootstrap study and applied to data from 13 published culpability studies.

Results: The model outperforms the culpability OR in bootstrap analyses. Used on actual study data, the average increase in crash risk is estimated at 1.28 (1.16–1.40). The pooled increased …