Evidence map›Paper›PMID 33737984›Full record

ArticleStatistics in biosciences2021

A general approach to sensitivity analysis for Mendelian randomization.

Weiming Zhang, Debashis Ghosh

Abstract read
In one paragraph

Article in Statistics in biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–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

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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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.

Weiming ZhangDepartment of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, U.S.A.
Debashis GhoshDepartment of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, U.S.A.

Funding

Statistical Tests for Mapping Genetic Determinants of Complex TraitsR01GM117946 · NIGMS · EMORY UNIVERSITY · PI EPSTEIN, MICHAEL PHILIP, GHOSH, DEBASHIS · 2016 to 2019
$1.2M
Medical Research Council MC_PC_15018Medical Research Council MC_PC_19009NIGMS NIH HHS R01 GM117946
6 · The paper itself

Abstract

Mendelian Randomization (MR) represents a class of instrumental variable methods using genetic variants. It has become popular in epidemiological studies to account for the unmeasured confounders when estimating the effect of exposure on outcome. The success of Mendelian Randomization depends on three critical assumptions, which are difficult to verify. Therefore, sensitivity analysis methods are needed for evaluating results and making plausible conclusions. We propose a general and easy to apply approach to conduct sensitivity analysis for Mendelian Randomization studies. Bound et al. (1995) derived a formula for the asymptotic bias of the instrumental variable estimator. Based on their work, we derive a new sensitivity analysis formula. The parameters in the formula include sensitivity parameters such as the correlation between instruments and unmeasured confounder, the direct effect of instruments on outcome and the strength of instruments. In our simulation studies, we examined our approach in various scenarios using either individual SNPs or unweighted allele score as instruments. By using a previously published dataset from researchers involving a bone mineral density study, we demonstrate that our proposed method is a useful tool for MR studies, and that investigators can combine their domain knowledge with our method to obtain bias-corrected results and make informed conclusions on the scientific plausibility of their findings.

Indexed as

causal inferenceinstrumental variablesensitivity analysisunmeasured confounding

Identifiers

PMID33737984
PMCPMC7962901

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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 OpenQuestion graph.