Imperial College London
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DrCosettaMinelli

Faculty of Medicine, National Heart & Lung Institute

Emeritus Reader in Medical Statistics
 
 
 
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Contact

 

cosetta.minelli1 Website

 
 
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Location

 

G 49Emmanuel Kaye BuildingRoyal Brompton Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bowden:2019:ije/dyy258,
author = {Bowden, J and Del, Greco M F and Minelli, C and Zhao, Q and Lawlor, DA and Sheehan, NA and Thompson, J and Davey, Smith G},
doi = {ije/dyy258},
journal = {International Journal of Epidemiology},
pages = {728--742},
title = {Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption},
url = {http://dx.doi.org/10.1093/ije/dyy258},
volume = {48},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background: Two-sample summary-data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated. Methods: Causal estimation and heterogeneity assessment in MR require an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular 'first-order' weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, ostensibly more accurate 'second-order' weights can dramatically increase the chances of failing to detect heterogeneity when it is truly present. We derive modified weights to mitigate both of these adverse effects. Results: Using Monte Carlo simulations, we show that the modified weights outperform first- and second-order weights in terms of heterogeneity quantification. Modified weights are also shown to remove the phenomenon of regression dilution bias in MR estimates obtained from weak instruments, unlike those obtained using first- and second-order weights. However, with small numbers of weak instruments, this comes at the cost of a reduction in estimate precision and power to detect a causal effect compared with first-order weighting. Moreover, first-order weights always furnish unbiased estimates and preserve the type I error rate under the causal null. We illustrate the utility of the new method using data from a recent two-sample summary-data MR analysis to assess the causal role of systolic blood pressure on coronary heart disease risk. Conclusions: We propose the use of modified weights within two-sample summary-data MR studies for accurately quantifying het
AU  - Bowden,J
AU  - Del,Greco M F
AU  - Minelli,C
AU  - Zhao,Q
AU  - Lawlor,DA
AU  - Sheehan,NA
AU  - Thompson,J
AU  - Davey,Smith G
DO  - ije/dyy258
EP  - 742
PY  - 2019///
SN  - 1464-3685
SP  - 728
TI  - Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption
T2  - International Journal of Epidemiology
UR  - http://dx.doi.org/10.1093/ije/dyy258
UR  - https://www.ncbi.nlm.nih.gov/pubmed/30561657
UR  - http://hdl.handle.net/10044/1/66325
VL  - 48
ER  -
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