The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research.

In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the &#...

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Autores principales:	Jack Bowden, Luke C Pilling, Deniz Türkmen, Chia-Ling Kuo, David Melzer
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/8edadb6867a74942bd7f7485670e41c3
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spelling	oai:doaj.org-article:8edadb6867a74942bd7f7485670e41c32021-12-02T20:03:21ZThe Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research.1553-73901553-740410.1371/journal.pgen.1009783https://doaj.org/article/8edadb6867a74942bd7f7485670e41c32021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pgen.1009783https://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the 'genetically moderated treatment effect' (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and a modified confounder adjustment method can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis. We call our framework 'Triangulation WIthin a STudy' (TWIST)' in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimates that are approximately uncorrelated, but reliant on different sets of assumptions. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease.Jack BowdenLuke C PillingDeniz TürkmenChia-Ling KuoDavid MelzerPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 17, Iss 9, p e1009783 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Jack Bowden Luke C Pilling Deniz Türkmen Chia-Ling Kuo David Melzer The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research.
description	In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the 'genetically moderated treatment effect' (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and a modified confounder adjustment method can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis. We call our framework 'Triangulation WIthin a STudy' (TWIST)' in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimates that are approximately uncorrelated, but reliant on different sets of assumptions. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease.
format	article
author	Jack Bowden Luke C Pilling Deniz Türkmen Chia-Ling Kuo David Melzer
author_facet	Jack Bowden Luke C Pilling Deniz Türkmen Chia-Ling Kuo David Melzer
author_sort	Jack Bowden
title	The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research.
title_short	The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research.
title_full	The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research.
title_fullStr	The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research.
title_full_unstemmed	The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research.
title_sort	triangulation within a study (twist) framework for causal inference within pharmacogenetic research.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/8edadb6867a74942bd7f7485670e41c3
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The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research.

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