Evidence map›Paper›PMID 32249995›Full record

ArticleGenetic epidemiology2020

A comparison of robust Mendelian randomization methods using summary data.

Eric A W Slob, Stephen Burgess

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetic epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 408 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
408citing papers in PubMed, 8 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

408 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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  20. Reassessing Instrument Strength in Two-Sample Mendelian Randomization Analysis.medRxiv : the preprint server for health sciences · 2026
    Article

348 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Eric A W SlobErasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.ORCID 0000-0002-1644-0735
Stephen BurgessDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.ORCID 0000-0001-5365-8760

Funding

Medical Research Council MC_UU_00002/7Wellcome Trust 204623/Z/16/Z
6 · The paper itself

Abstract

The number of Mendelian randomization (MR) analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. Since it is unlikely that all genetic variants will be valid instrumental variables, several robust methods have been proposed. We compare nine robust methods for MR based on summary data that can be implemented using standard statistical software. Methods were compared in three ways: by reviewing their theoretical properties, in an extensive simulation study, and in an empirical example. In the simulation study, the best method, judged by mean squared error was the contamination mixture method. This method had well-controlled Type 1 error rates with up to 50% invalid instruments across a range of scenarios. Other methods performed well according to different metrics. Outlier-robust methods had the narrowest confidence intervals in the empirical example. With isolated exceptions, all methods performed badly when over 50% of the variants were invalid instruments. Our recommendation for investigators is to perform a variety of robust methods that operate in different ways and rely on different assumptions for valid inferences to assess the reliability of MR analyses.

Indexed as

Body Mass IndexCoronary Artery DiseaseGenetic Association StudiesGenetic PleiotropyHumansMendelian Randomization AnalysisOdds RatioRisk Factorscausal inferenceMendelian randomizationpleiotropyrobust estimationsummary statistics

Identifiers

PMID32249995
PMCPMC7317850

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.