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Cited by 1,565

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic

Open access
Bowden, Jack, Del Greco M., Fabiola, Minelli, Cosetta, Davey Smith, George, Sheehan, Nuala A., Thompson, John R.·2016·International Journal of Epidemiology
DOI 10.1093/ije/dyw220
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Abstract

Background: : MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error' (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. Methods: An adaptation of the I2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it IGX2 . The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. Results: In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of IGX2 ), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We demonstrate our proposed approach for a two-sample summary data MR analysis to estimate the causal effect of low-density lipoprotein on heart disease risk. A high value of IGX2 close to 1 indicates that dilution does not materially affect the standard MR-Egger analyses for these data. Conclusions: : Care must be taken to assess the NOME assumption via the IGX2 statistic before implementing standard MR-Egger regression in the two-sample summary data context. If IGX2 is sufficiently low (less than 90%), inferences from the method should be interpreted with caution and adjustment methods considered.

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Show all 9 references

Research works printed but not linked: 18

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  17. Sharp SJ. Analysing the relationship between treatment benefit and underlying risk: precautions and recommendations. In: Egger M Davey Smith G Altman DG (eds). Systematic Reviews in Healthcare: Meta-analysis in Context.. 2nd edn. Chichester, UK: Wiley, 2008.
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Metadata

Authors
Bowden, J., Del Greco M., F., Minelli, C., Davey Smith, G., Sheehan, N. A., & Thompson, J. R.
Venue
International Journal of Epidemiology
Publisher
Oxford University Press (OUP)
Published
2016-09-11
Volume
45
Issue
6
Pages
dyw220
ISSN
  • 0300-5771 (ISSN-L)
  • 1464-3685
DOI
10.1093/ije/dyw220
OpenAlex
W2508534964
Cited by
1,565
References
9
FWCI
37.43
Type
journal article
Language
en
Open access
hybrid

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