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A review of instrumental variable estimators for Mendelian randomization.

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Small, Dylan S 
Thompson, Simon G 


Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure-outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure. Under the assumption that a single instrumental variable or a set of instrumental variables for the exposure is available, the causal effect of the exposure on the outcome can be estimated. There are several methods available for instrumental variable estimation; we consider the ratio method, two-stage methods, likelihood-based methods, and semi-parametric methods. Techniques for obtaining statistical inferences and confidence intervals are presented. The statistical properties of estimates from these methods are compared, and practical advice is given about choosing a suitable analysis method. In particular, bias and coverage properties of estimators are considered, especially with weak instruments. Settings particularly relevant to Mendelian randomization are prioritized in the paper, notably the scenario of a continuous exposure and a continuous or binary outcome.



Instrumental variable, Mendelian randomization, causal inference, comparison of methods, finite-sample bias, weak instruments, Bayes Theorem, Case-Control Studies, Causality, Confidence Intervals, Genetic Variation, Humans, Least-Squares Analysis, Likelihood Functions, Mendelian Randomization Analysis, Models, Statistical, Risk Factors

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Stat Methods Med Res

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SAGE Publications
Medical Research Council (MR/L003120/1)
British Heart Foundation (CH/12/2/29428)
British Heart Foundation (None)
Wellcome Trust (100114/Z/12/Z)
Stephen Burgess is supported by the Wellcome Trust (grant number 100114). Dylan Small was supported by a grant from the US National Science Foundation Measurement, Methodology and Statistics program. Simon G. Thompson is supported by the British Heart Foundation (grant number CH/12/2/29428).