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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2015
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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. |
format | Online Article Text |
id | pubmed-4856212 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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