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Application of causal inference methods in the analyses of randomised controlled trials: a systematic review

BACKGROUND: Applications of causal inference methods to randomised controlled trial (RCT) data have usually focused on adjusting for compliance with the randomised intervention rather than on using RCT data to address other, non-randomised questions. In this paper we review use of causal inference m...

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Autores principales: Farmer, Ruth E., Kounali, Daphne, Walker, A. Sarah, Savović, Jelena, Richards, Alison, May, Margaret T., Ford, Deborah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5761133/
https://www.ncbi.nlm.nih.gov/pubmed/29321046
http://dx.doi.org/10.1186/s13063-017-2381-x
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author Farmer, Ruth E.
Kounali, Daphne
Walker, A. Sarah
Savović, Jelena
Richards, Alison
May, Margaret T.
Ford, Deborah
author_facet Farmer, Ruth E.
Kounali, Daphne
Walker, A. Sarah
Savović, Jelena
Richards, Alison
May, Margaret T.
Ford, Deborah
author_sort Farmer, Ruth E.
collection PubMed
description BACKGROUND: Applications of causal inference methods to randomised controlled trial (RCT) data have usually focused on adjusting for compliance with the randomised intervention rather than on using RCT data to address other, non-randomised questions. In this paper we review use of causal inference methods to assess the impact of aspects of patient management other than the randomised intervention in RCTs. METHODS: We identified papers that used causal inference methodology in RCT data from Medline, Premedline, Embase, Cochrane Library, and Web of Science from 1986 to September 2014, using a forward citation search of five seminal papers, and a keyword search. We did not include studies where inverse probability weighting was used solely to balance baseline characteristics, adjust for loss to follow-up or adjust for non-compliance to randomised treatment. Studies where the exposure could not be assigned were also excluded. RESULTS: There were 25 papers identified. Nearly half the papers (11/25) estimated the causal effect of concomitant medication on outcome. The remainder were concerned with post-randomisation treatment regimens (sequential treatments, n =5 ), effects of treatment timing (n = 2) and treatment dosing or duration (n = 7). Examples were found in cardiovascular disease (n = 5), HIV (n = 7), cancer (n = 6), mental health (n = 4), paediatrics (n = 2) and transfusion medicine (n = 1). The most common method implemented was a marginal structural model with inverse probability of treatment weighting. CONCLUSIONS: Examples of studies which exploit RCT data to address non-randomised questions using causal inference methodology remain relatively limited, despite the growth in methodological development and increasing utilisation in observational studies. Further efforts may be needed to promote use of causal methods to address additional clinical questions within RCTs to maximise their value. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13063-017-2381-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-57611332018-01-16 Application of causal inference methods in the analyses of randomised controlled trials: a systematic review Farmer, Ruth E. Kounali, Daphne Walker, A. Sarah Savović, Jelena Richards, Alison May, Margaret T. Ford, Deborah Trials Review BACKGROUND: Applications of causal inference methods to randomised controlled trial (RCT) data have usually focused on adjusting for compliance with the randomised intervention rather than on using RCT data to address other, non-randomised questions. In this paper we review use of causal inference methods to assess the impact of aspects of patient management other than the randomised intervention in RCTs. METHODS: We identified papers that used causal inference methodology in RCT data from Medline, Premedline, Embase, Cochrane Library, and Web of Science from 1986 to September 2014, using a forward citation search of five seminal papers, and a keyword search. We did not include studies where inverse probability weighting was used solely to balance baseline characteristics, adjust for loss to follow-up or adjust for non-compliance to randomised treatment. Studies where the exposure could not be assigned were also excluded. RESULTS: There were 25 papers identified. Nearly half the papers (11/25) estimated the causal effect of concomitant medication on outcome. The remainder were concerned with post-randomisation treatment regimens (sequential treatments, n =5 ), effects of treatment timing (n = 2) and treatment dosing or duration (n = 7). Examples were found in cardiovascular disease (n = 5), HIV (n = 7), cancer (n = 6), mental health (n = 4), paediatrics (n = 2) and transfusion medicine (n = 1). The most common method implemented was a marginal structural model with inverse probability of treatment weighting. CONCLUSIONS: Examples of studies which exploit RCT data to address non-randomised questions using causal inference methodology remain relatively limited, despite the growth in methodological development and increasing utilisation in observational studies. Further efforts may be needed to promote use of causal methods to address additional clinical questions within RCTs to maximise their value. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13063-017-2381-x) contains supplementary material, which is available to authorized users. BioMed Central 2018-01-10 /pmc/articles/PMC5761133/ /pubmed/29321046 http://dx.doi.org/10.1186/s13063-017-2381-x Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Review
Farmer, Ruth E.
Kounali, Daphne
Walker, A. Sarah
Savović, Jelena
Richards, Alison
May, Margaret T.
Ford, Deborah
Application of causal inference methods in the analyses of randomised controlled trials: a systematic review
title Application of causal inference methods in the analyses of randomised controlled trials: a systematic review
title_full Application of causal inference methods in the analyses of randomised controlled trials: a systematic review
title_fullStr Application of causal inference methods in the analyses of randomised controlled trials: a systematic review
title_full_unstemmed Application of causal inference methods in the analyses of randomised controlled trials: a systematic review
title_short Application of causal inference methods in the analyses of randomised controlled trials: a systematic review
title_sort application of causal inference methods in the analyses of randomised controlled trials: a systematic review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5761133/
https://www.ncbi.nlm.nih.gov/pubmed/29321046
http://dx.doi.org/10.1186/s13063-017-2381-x
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