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Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies

BACKGROUND: Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study heterogeneity of two association studies required in MR is often overlooked. When dealing with large studies, recently devel...

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Autores principales: Zou, Linyi, Guo, Hui, Berzuini, Carlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9164425/
https://www.ncbi.nlm.nih.gov/pubmed/35658839
http://dx.doi.org/10.1186/s12874-022-01619-4
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author Zou, Linyi
Guo, Hui
Berzuini, Carlo
author_facet Zou, Linyi
Guo, Hui
Berzuini, Carlo
author_sort Zou, Linyi
collection PubMed
description BACKGROUND: Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study heterogeneity of two association studies required in MR is often overlooked. When dealing with large studies, recently developed Bayesian MR can be computationally challenging, and sometimes even prohibitive. METHODS: We addressed study heterogeneity by proposing a random effect Bayesian MR model with multiple exposures and outcomes. For large studies, we adopted a subset posterior aggregation method to overcome the problem of computational expensiveness of Markov chain Monte Carlo. In particular, we divided data into subsets and combined estimated causal effects obtained from the subsets. The performance of our method was evaluated by a number of simulations, in which exposure data was partly missing. RESULTS: Random effect Bayesian MR outperformed conventional inverse-variance weighted estimation, whether the true causal effects were zero or non-zero. Data partitioning of large studies had little impact on variations of the estimated causal effects, whereas it notably affected unbiasedness of the estimates with weak instruments and high missing rate of data. For the cases being simulated in our study, the results have indicated that the “divide (data) and combine (estimated subset causal effects)” can help improve computational efficiency, for an acceptable cost in terms of bias in the causal effect estimates, as long as the size of the subsets is reasonably large. CONCLUSIONS: We further elaborated our Bayesian MR method to explicitly account for study heterogeneity. We also adopted a subset posterior aggregation method to ease computational burden, which is important especially when dealing with large studies. Despite the simplicity of the model we have used in the simulations, we hope the present work would effectively point to MR studies that allow modelling flexibility, especially in relation to the integration of heterogeneous studies and computational practicality.
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spelling pubmed-91644252022-06-05 Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies Zou, Linyi Guo, Hui Berzuini, Carlo BMC Med Res Methodol Research BACKGROUND: Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study heterogeneity of two association studies required in MR is often overlooked. When dealing with large studies, recently developed Bayesian MR can be computationally challenging, and sometimes even prohibitive. METHODS: We addressed study heterogeneity by proposing a random effect Bayesian MR model with multiple exposures and outcomes. For large studies, we adopted a subset posterior aggregation method to overcome the problem of computational expensiveness of Markov chain Monte Carlo. In particular, we divided data into subsets and combined estimated causal effects obtained from the subsets. The performance of our method was evaluated by a number of simulations, in which exposure data was partly missing. RESULTS: Random effect Bayesian MR outperformed conventional inverse-variance weighted estimation, whether the true causal effects were zero or non-zero. Data partitioning of large studies had little impact on variations of the estimated causal effects, whereas it notably affected unbiasedness of the estimates with weak instruments and high missing rate of data. For the cases being simulated in our study, the results have indicated that the “divide (data) and combine (estimated subset causal effects)” can help improve computational efficiency, for an acceptable cost in terms of bias in the causal effect estimates, as long as the size of the subsets is reasonably large. CONCLUSIONS: We further elaborated our Bayesian MR method to explicitly account for study heterogeneity. We also adopted a subset posterior aggregation method to ease computational burden, which is important especially when dealing with large studies. Despite the simplicity of the model we have used in the simulations, we hope the present work would effectively point to MR studies that allow modelling flexibility, especially in relation to the integration of heterogeneous studies and computational practicality. BioMed Central 2022-06-03 /pmc/articles/PMC9164425/ /pubmed/35658839 http://dx.doi.org/10.1186/s12874-022-01619-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zou, Linyi
Guo, Hui
Berzuini, Carlo
Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies
title Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies
title_full Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies
title_fullStr Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies
title_full_unstemmed Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies
title_short Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies
title_sort bayesian mendelian randomization with study heterogeneity and data partitioning for large studies
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9164425/
https://www.ncbi.nlm.nih.gov/pubmed/35658839
http://dx.doi.org/10.1186/s12874-022-01619-4
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