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Mendelian Randomization is a widely used instrumental variable method for assessing causal effects of lifelong exposures on health outcomes. Many exposures, however, have causal effects that vary across the life course and often influence…

Recent advances in genotyping technology have delivered a wealth of genetic data, which is rapidly advancing our understanding of the underlying genetic architecture of complex diseases. Mendelian Randomization (MR) leverages such genetic…

Methodology · Statistics 2023-12-19 Wenhao Cao , Saonli Basu

Mendelian randomization uses genetic variants to make causal inferences about a modifiable exposure. Subject to a genetic variant satisfying the instrumental variable assumptions, an association between the variant and outcome implies a…

Methodology · Statistics 2018-04-17 Stephen Burgess , Jeremy A Labrecque

The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental…

Methodology · Statistics 2016-08-03 T. Martinussen , S. Vansteelandt , E. J. Tchetgen Tchetgen , D. M. Zucker

Mendelian randomization is a widely-used method to estimate the unconfounded effect of an exposure on an outcome by using genetic variants as instrumental variables. Mendelian randomization analyses which use variants from a single genetic…

Methodology · Statistics 2024-02-20 Ashish Patel , Dipender Gill , Paul J. Newcombe , Stephen Burgess

Mendelian randomization (MR) has become an essential tool for causal inference in biomedical and public health research. By using genetic variants as instrumental variables, MR helps address unmeasured confounding and reverse causation,…

Methodology · Statistics 2025-11-04 Minhao Yao , Anqi Wang , Xihao Li , Zhonghua Liu

Mendelian randomization is the use of genetic variants to make causal inferences from observational data. The field is currently undergoing a revolution fuelled by increasing numbers of genetic variants demonstrated to be associated with…

Methodology · Statistics 2018-08-31 Stephen Burgess , Jack Bowden , Frank Dudbridge , Simon G Thompson

In this paper we review an approach to estimating the causal effect of a time-varying treatment on time to some event of interest. This approach is designed for the situation where the treatment may have been repeatedly adapted to patient…

Statistics Theory · Mathematics 2007-06-13 J. J. Lok , R. D. Gill , A. W. van der Vaart , J. M. Robins

Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of a modifiable exposure on an outcome. Although recent years have seen many extensions of basic MR…

Methodology · Statistics 2022-03-15 Sai Li , Ting Ye

This article studies the estimation of the causal effect of a time-varying treatment on time-to-an-event or on some other continuously distributed outcome. The paper applies to the situation where treatment is repeatedly adapted to…

Statistics Theory · Mathematics 2008-06-19 Judith J. Lok

Valid estimation of a causal effect using instrumental variables requires that all of the instruments are independent of the outcome conditional on the risk factor of interest and any confounders. In Mendelian randomization studies with…

Methodology · Statistics 2020-11-23 Andrew J. Grant , Stephen Burgess

Estimating the causal effect of an exposure on an outcome is an important task in many economical and biological studies. Mendelian randomization, in particular, uses genetic variants as instruments to estimate causal effects in…

Methodology · Statistics 2017-06-06 Sai Li

Mendelian randomization uses genetic variants to make causal inferences about the effect of a risk factor on an outcome. With fine-mapped genetic data, there may be hundreds of genetic variants in a single gene region any of which could be…

Methodology · Statistics 2017-07-10 Stephen Burgess , Verena Zuber , Elsa Valdes-Marquez , Benjamin B Sun , Jemma C Hopewell

Mendelian randomization (MR) is a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome by using genetic instruments. These instruments are often selected from a combination of prior knowledge from…

Methodology · Statistics 2019-11-12 Nan Bi , Hyunseung Kang , Jonathan Taylor

The method of multivariable Mendelian randomization uses genetic variants to instrument multiple exposures, to estimate the effect that a given exposure has on an outcome conditional on all other exposures included in a linear model.…

Methodology · Statistics 2024-08-20 Ashish Patel , James Lane , Stephen Burgess

The traditional model specification of stepped-wedge cluster-randomized trials assumes a homogeneous treatment effect across time while adjusting for fixed-time effects. However, when treatment effects vary over time, the constant effect…

The use of genetic variants as instrumental variables - an approach known as Mendelian randomization - is a popular epidemiological method for estimating the causal effect of an exposure (phenotype, biomarker, risk factor) on a disease or…

Methodology · Statistics 2020-12-21 Ioan Gabriel Bucur , Tom Claassen , Tom Heskes

Mendelian randomization is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data Mendelian randomization analyses with many…

Quantitative Methods · Quantitative Biology 2022-09-16 Apostolos Gkatzionis , Stephen Burgess , Paul J. Newcombe

Two-sample summary-data Mendelian randomization (MR) has become a popular research design to estimate the causal effect of risk exposures. With the sample size of GWAS continuing to increase, it is now possible to utilize genetic…

Applications · Statistics 2018-11-20 Qingyuan Zhao , Yang Chen , Jingshu Wang , Dylan S. Small

Mendelian randomization (MR) uses genetic variants as instrumental variables to make causal claims. Standard MR approaches typically report a single population-averaged estimate, limiting their ability to explore effect heterogeneity or…

Methodology · Statistics 2025-07-16 Stephen Burgess , Benjamin A R Woolf , Amy M Mason
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