Bayesian regression discontinuity designs: Incorporating clinical knowledge in the causal analysis of primary care data. Geneletti, S, O'Keeffe, AG, Sharples, LD, Richardson, S, & Baio, G 2014. ArXiv e-prints
Bayesian regression discontinuity designs: Incorporating clinical knowledge in the causal analysis of primary care data [link]Paper  abstract   bibtex   
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. NHS guidelines state that statins should be prescribed to patients with 10 year cardiovascular disease risk scores in excess of 20%. If we consider patients whose scores are close to this threshold we find that there is an element of random variation in both the risk score itself and its measurement. We can thus consider the threshold a randomising device assigning the prescription to units just above the threshold and withholds it from those just below. Thus we are effectively replicating the conditions of an RCT in the area around the threshold, removing or at least mitigating confounding. We frame the RD design in the language of conditional independence which clarifies the assumptions necessary to apply it to data, and which makes the links with instrumental variables clear. We also have context specific knowledge about the expected sizes of the effects of statin prescription and are thus able to incorporate this into Bayesian models by formulating informative priors on our causal parameters.
@UNPUBLISHED{Genelettietal:2014,
author = {Geneletti, S and {O'Keeffe, AG} and {Sharples, LD} and Richardson, S
	and Baio, G},
note = "{ArXiv e-prints}",
title = {Bayesian regression discontinuity designs: Incorporating clinical
	knowledge in the causal analysis of primary care data},
abstract = {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.
	NHS guidelines state that statins should be prescribed to patients
	with 10 year cardiovascular disease risk scores in excess of 20\%.
	If we consider patients whose scores are close to this threshold
	we find that there is an element of random variation in both the
	risk score itself and its measurement. We can thus consider the threshold
	a randomising device assigning the prescription to units just above
	the threshold and withholds it from those just below. Thus we are
	effectively replicating the conditions of an RCT in the area around
	the threshold, removing or at least mitigating confounding. We frame
	the RD design in the language of conditional independence which clarifies
	the assumptions necessary to apply it to data, and which makes the
	links with instrumental variables clear. We also have context specific
	knowledge about the expected sizes of the effects of statin prescription
	and are thus able to incorporate this into Bayesian models by formulating
	informative priors on our causal parameters.},
  keyword = {stat.ME},
  url = {http://arxiv.org/abs/1403.1806},
  year = 2014,
  pubstate = {Later published in \textit{Statistics in Medicine}}
}

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