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Health policy researchers often have questions about the effects of a policyimplemented at some cluster-level unit, e.g., states, counties, hospitals, etc.on individual-level outcomes collected over multiple time periods. Stackeddifference-in-differences is an increasingly popular way to estimate theseeffects. This approach involves estimating treatment effects for eachpolicy-implementing unit, then, if scientifically appropriate, aggregating themto an average effect estimate. However, when individual-level data areavailable and non-implementing units are used as comparators for multiplepolicy-implementing units, data from untreated individuals may be used acrossmultiple analyses, thereby inducing correlation between effect estimates.Existing methods do not quantify or account for this sharing of controls. Here,we describe a stacked difference-in-differences study investigating the effectsof state medical cannabis laws on treatment for chronic pain management thatmotivated this work, discuss a framework for estimating and managing thiscorrelation due to shared control individuals, and show how accounting for itaffects the substantive results.

Shared Control Individuals in Health Policy Evaluations with Application to Medical Cannabis Laws