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State-level policy evaluations commonly employ a difference-in-differences(DID) study design; yet within this framework, statistical model specificationvaries notably across studies. Motivated by applied state-level opioid policyevaluations, this simulation study compares statistical performance of multiplevariations of two-way fixed effect models traditionally used for DID under arange of simulation conditions. While most linear models resulted in minimalbias, non-linear models and population-weighted versions of classic lineartwo-way fixed effect and linear GEE models yielded considerable bias (60 to160%). Further, root mean square error is minimized by linear AR models whenexamining crude mortality rates and by negative binomial models when examiningraw death counts. In the context of frequentist hypothesis testing, many modelsyielded high Type I error rates and very low rates of correctly rejecting thenull hypothesis (< 10%), raising concerns of spurious conclusions about policyeffectiveness. When considering performance across models, the linearautoregressive models were optimal in terms of directional bias, root meansquared error, Type I error, and correct rejection rates. These findingshighlight notable limitations of traditional statistical models commonly usedfor DID designs, designs widely used in opioid policy studies and in statepolicy evaluations more broadly.

Moving beyond the classic difference-in-differences model: A simulation  study comparing statistical methods for estimating effectiveness of  state-level policies