Mendelian randomization as an instrumental variable approach to causal inference

V Didelez, N Sheehan

Research output: Contribution to journalArticle (Academic Journal)peer-review

333 Citations (Scopus)

Abstract

In epidemiological research, the causal effect of a modifiable phenotype or exposure on a disease is often of public health interest. Randomized controlled trials to investigate this effect are not always possible and inferences based on observational data can be confounded. However, if we know of a gene closely linked to the phenotype without direct effect on the disease, it can often be reasonably assumed that the gene is not itself associated with any confounding factors — a phenomenon called Mendelian randomization. These properties define an instrumental variable and allow estimation of the causal effect, despite the confounding, under certain model restrictions. In this paper, we present a formal framework for causal inference based on Mendelian randomization and suggest using directed acyclic graphs to check model assumptions by visual inspection. This framework allows us to address limitations of the Mendelian randomization technique that have often been overlooked in the medical literature.
Translated title of the contributionMendelian randomization as an instrumental variable approach to causal inference
Original languageEnglish
Pages (from-to)309 - 330
Number of pages22
JournalStatistical Methods in Medical Research
Volume16 (4)
DOIs
Publication statusPublished - Aug 2007

Bibliographical note

Publisher: Sage

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