Identifying Heterogeneous Treatment Effects of Drug Policy in Quasi-experimental Settings

AbstractPurpose of ReviewWe sought to describe the difference-in-differences study design and how they can be applied to identify the average treatment effect. We then extend this approach to identify heterogeneity in treatment effects based on (1) an individuals ’ baseline risk of an event using risk scores, (2) the outcome distribution using quantile regression, and (3) prior trajectories of outcomes using group-based trajectory models.Recent FindingsThe methods for the identification of heterogeneous treatment effect have developed in ways that can provide researchers and policymakers a more nuanced understanding of treatment effects.SummaryRecent analytic advances found in other fields should be adopted and tested by pharmacoepidemiology and drug policy researcher to better understand the effects of new policies and interventions.

Source: Current Epidemiology Reports - July 17, 2019 Category: Epidemiology Source Type: research