Highlights Not adjusting for low driving mileage as a confounder may lead to an overestimation of the effect of polypharmacy on road traffic crashes. The magnitude of confounding by low driving mileage on the effect of polypharmacy on RTCs was 16%. An adjusted increase of the odds of RTCs of at least 60% is associated with a statistical power of about 80%.

    Abstract Introduction Unmeasured confounding can lead to biased interpretations of empirical findings. This paper aimed to assess the magnitude of suspected unmeasured confounding due to driving mileage and simulate the statistical power required to detect a discrepancy in the effect of polypharmacy on road traffic crashes (RTCs) among older adults. Methods Based on Monte Carlo Simulation (MCS) approach, we estimated 1) the magnitude of confounding of driving mileage on the association of polypharmacy and RTCs and 2) the statistical power of to detect a discrepancy from no adjusted effect. A total of 1000 studies, each of 500000 observations, were simulated. Results Under the assumption of a modest adjusted exposure-outcome odds ratio of 1.35, the magnitude of confounding bias by driving mileage was estimated to be 16% higher with a statistical power of 50%. Only an adjusted odds ratio of at least 1.60 would be associated with a statistical power of about 80% Conclusion This applied probabilistic bias analysis showed that not adjusting for driving mileage as a confounder can lead to an overestimation of the effect of polypharmacy on RTCs in older adults. Even considering a large sample, small to moderate adjusted exposure effects were difficult to be detected.


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    Title :

    A probabilistic bias analysis on the magnitude of unmeasured confounding: The impact of driving mileage on road traffic crashes


    Contributors:


    Publication date :

    2023-05-26




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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