A review of instrumental variable estimators for Mendelian randomization

By Stephen Burgess, Dylan S. Small, S. Thompson. Statistical Methods in Medical Research, 2015

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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.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0497ef202c781093fb10c56d9206d34cc86fb3e5

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Citations in the corpus

Meta‐analysis and Mendelian randomization: A review (2019, methodology): “mated via a Taylor series expansion of bβR using the delta method.(3) The ratio estimate in (2) is calculated from two summary estimates, but it is also equivalent to the estimate obtained by the following two‐step procedure applied to individual level data on Y, X, and G:”
Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, background): “have appeared recently that relate to the scope of MR (10,11), emerging methods (11,12), applications to drug discovery (13– 15), and comparisons to other causal methods (16–18).”