The Regression Discontinuity Design: a novel approach to evaluating the effect of drugs and treatments in primary care

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A fundamental task in clinical practice is to determine whether a particular drug is being prescribed in the most effective way, in order to increase the clinical benefit derived from its use. While Randomised Clinical Trials (RCTs) are correctly considered to be the best scientific method for evaluation of drug efficacy, these studies often have poor external validity, because of patient selection, in particular the avoidance of comorbidities. Prescription guidelines are not always evidence based and it typically falls to clinical experts to set them. The regression discontinuity design (RDD) is an econometric quasi-experimental design aimed at estimating the causal effects of a treatment by exploiting naturally occurring treatment rules. It was first introduced in the educational economics literature in the 1960s but it has not been widely used outside of this field until recently. The RDD exploits the fact that many treatments are assigned according to pre-decided rules, eg those set by NICE in the UK. The idea behind the RDD is that if we can assume that individuals just on either side of a pre-selected threshold (eg blood pressure of 140/90mmHg) belong to a common population with respect to the characteristics that inform the assignment rule and determine the outcome, then the threshold can be seen as a random intervention which assigns the treatment to those that fall just above and no treatment to those that fall just below it. Due to the quasi-randomised nature of the RDD, confounding is eliminated or at least mitigated. We can thus use the RDD to estimate the effect of the treatment or a related exposure within the population around the threshold. However, although the RDD mimics randomisation, in our particular case, in order to reduce bias of the estimates it will still be necessary to further account in the analyses for other factors which may impact the decision to prescribe, including markers of patients frailty or comorbidities. In situations where the set up is fully adhered to (ie when all individuals above (below) the threshold are (not) given the treatment, the RDD is termed "sharp". Conversely, in cases where well-defined guidelines exist, but for some (possibly completely legit, under a clinical perspective) reason they are not fully followed, the RDD is termed "fuzzy". RDD estimators have strong connections with other causal inference estimators in statistical research. However, in our case, we are faced with the complication that often in epidemiology and clinical research the outcome of interest is represented by a binary variable (eg the occurrence of a clinical event, or mortality). Specific methods for such a case have not been extensively developed, although Instrumental Variable (IV) theory can be brought to bear. Specifically, the main difficulty lies in the fact that in this case the interest is in causal ratios, rather than causal differences (which are relevant in the case of continuous outcomes). Methods that can be used to derive such estimators are based on making additional assumptions in addition to the standard IV setting. Assumption A1) involves approximate linearity between a function of the outcome and the treatment and unobserved confounders; and A2) assumes no interaction between the treatment and the unobserved confounders (termed "no-effect modification on the multiplicative scale"). These cannot be directly tested (because they involve unobservable variables), but we will investigate their plausibility using simulations. Electronic healthcare records such as "The Health Improvement Network" (THIN) primary care database provide an excellent opportunity to investigate questions like those mentioned above. From THIN we can obtain detailed information on prescriptions made in primary care and measurements both before and after initiation of prescription, as well as adverse events.