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...

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Autores principales: Geneletti, Sara, O'Keeffe, Aidan G., Sharples, Linda D., Richardson, Sylvia, Baio, Gianluca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4856212/
https://www.ncbi.nlm.nih.gov/pubmed/25809691
http://dx.doi.org/10.1002/sim.6486
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author Geneletti, Sara
O'Keeffe, Aidan G.
Sharples, Linda D.
Richardson, Sylvia
Baio, Gianluca
author_facet Geneletti, Sara
O'Keeffe, Aidan G.
Sharples, Linda D.
Richardson, Sylvia
Baio, Gianluca
author_sort Geneletti, Sara
collection PubMed
description 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 guidelines in the UK state that statins should be prescribed to patients with 10‐year cardiovascular disease risk scores in excess of 20%. If we consider patients whose risk scores are close to the 20% 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 as a randomising device that assigns statin prescription to individuals 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 an RD design 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. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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spelling pubmed-48562122016-05-04 Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data Geneletti, Sara O'Keeffe, Aidan G. Sharples, Linda D. Richardson, Sylvia Baio, Gianluca Stat Med Research Articles 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 guidelines in the UK state that statins should be prescribed to patients with 10‐year cardiovascular disease risk scores in excess of 20%. If we consider patients whose risk scores are close to the 20% 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 as a randomising device that assigns statin prescription to individuals 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 an RD design 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. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. John Wiley and Sons Inc. 2015-03-24 2015-07-10 /pmc/articles/PMC4856212/ /pubmed/25809691 http://dx.doi.org/10.1002/sim.6486 Text en © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Geneletti, Sara
O'Keeffe, Aidan G.
Sharples, Linda D.
Richardson, Sylvia
Baio, Gianluca
Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data
title Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data
title_full Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data
title_fullStr Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data
title_full_unstemmed Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data
title_short Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data
title_sort bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4856212/
https://www.ncbi.nlm.nih.gov/pubmed/25809691
http://dx.doi.org/10.1002/sim.6486
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