Bayesian regression discontinuity designs: Incorporating clinical knowledge in the causal analysis of primary care data

The regression discontinuity (RD) design is a quasi-experimental design that estimates the causal effects of a treatment by exploiting naturally occurring treatment rules. It can be applied in any context where a particular treatment or intervention is administered according to a pre-specified rule linked to a continuous variable. Such thresholds are common in primary care drug prescription where the RD design can be used to estimate the causal effect of medication in the general population. Such results can then be contrasted to those obtained from randomised controlled trials (RCTs) and inform prescription policy and guidelines based on a more realistic and less expensive context. In this paper we focus on statins, a class of cholesterol-lowering drugs, however, the methodology can be applied to many other drugs provided these are prescribed in accordance to pre-determined guidelines. Current NHS guidelines state that statins should be prescribed to patients with 10 year cardiovascular disease risk scores in excess of twenty percent. If we consider patients whose risk scores are close to the twenty percent risk score threshold we find that there is an element of random variation in both the risk score itself and its measurement. We can therefore consider the threshold a randomising device that assigns statin prescription to individuals just above the threshold and withholds it from thos