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Multilevel models analysis with applications to assess dose-response effects of national essential medicine policy in China
The conventional difference-in-differences (DID) method are effective in measuring mean effects of a policy intervention between two time points, but is inefficient in measuring change of effects over multiple time points, nor measure variation of effects that might be attributable to contextual factors. Identifying dose-response effect of policy over time and variation beyond the main effects in the population could be much informative to police-making in targeting local implementation. This report aimed to propose multilevel repeated measures(RM) models to answer these questions.
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