Good policymaking depends on reliable evaluations of state-level policies. But reliable evaluations face multiple barriers, including whether statistical models can disentangle the effects of concurrently implemented policies.

To test the performance of commonly used models, the authors conducted simulations using outcome data (annual opioid mortality rate per 100,000 residents, by state) obtained from the National Vital Statistics System Multiple Cause of Death mortality files, from 1999 to 2016, for all 50 states. The authors modified the outcome data by adding the hypothetical effect of two co-occurring policies and examined how effective the models were at estimating the policy effects over a range of simulation parameters, such as the effect sizes of the policies and the length of time between their enactment dates.

This tool allows users to select from a range of simulation parameters to explore how different models perform and compare models by various performance metrics.

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This research was sponsored by the National Institute on Drug Abuse and conducted by RAND Health Care.

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