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Accid Anal Prev. 2018 Nov 20;123:69-78. doi: 10.1016/j.aap.2018.11.011. [Epub ahead of print]

A meta-analysis of the crash risk of cannabis-positive drivers in culpability studies-Avoiding interpretational bias.

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Frisch Centre, Norway. Electronic address:



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".


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.


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 risk of a culpable crash is estimated as 1.42 (95% credibility interval 1.11-1.75), which is similar to pooled estimates using traditional ORs (1.46, 95% CI: 1.24-1.72). The attributable risk fraction of cannabis impaired driving is estimated to lie below 2% for all but two of the included studies.


Culpability ORs exaggerate risk increases and parameter uncertainty when misinterpreted as total crash ORs. The increased crash risk associated with THC-positive drivers in culpability studies is low.


Bayesian inference; Cannabis; Crash risks; Culpability study; Meta-analysis

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