Strategies for evaluating the assumptions of the regression discontinuity design: a case study using a human papillomavirus vaccination programme

The regression discontinuity design (RDD) is a quasi-experimental approach used to avoid confounding bias in the assessment of new policies and interventions. It is applied specifically in situations where individuals are assigned to a policy/intervention based on whether they are above or below a pre-specified cut-off on a continuously measured variable, such as birth date, income or weight. The strength of the design is that, provided individuals do not manipulate the value of this variable, assignment to the policy/intervention is considered as good as random for individuals close to the cut-off. Despite its popularity in fields like economics, the RDD remains relatively unknown in epidemiology where its application could be tremendously useful. In this paper, we provide a practical introduction to the RDD for health researchers, describe four empirically testable assumptions of the design and offer strategies that can be used to assess whether these assumptions are met in a given study. For illustrative purposes, we implement these strategies to assess whether the RDD is appropriate for a study of the impact of human papillomavirus vaccination on cervical dysplasia. We found that, whereas the assumptions of the RDD were generally satisfied in our study context, birth timing had the potential to confound our effect estimate in an unexpected way and therefore needed to be taken into account in the analysis. Our findings underscore the importance of assessing the validity of the assumptions of this design, testing them when possible and making adjustments as necessary to support valid causal inference.