Robust instrumental variable methods using multiple candidate instruments with application to Mendelian randomization

TL;DR

This paper introduces robust instrumental variable methods for Mendelian randomization using multiple genetic variants, improving causal inference accuracy when some variants are invalid, through regression, penalization, and median-based approaches.

Contribution

The paper develops and compares three novel extensions of IV methods—robust regression, weight penalization, and L1 penalization—for Mendelian randomization with multiple candidate instruments.

Findings

Robust regression and median-based methods improve Type 1 error rates.

MR-Egger estimates are unbiased but less efficient and sensitive to assumption violations.

Using different methods routinely enhances robustness of causal findings.

Abstract

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 exposures in genome-wide association studies, and the public availability of summarized data on genetic associations with exposures and outcomes from large consortia. A Mendelian randomization analysis with many genetic variants can be performed relatively simply using summarized data. However, a causal interpretation is only assured if each genetic variant satisfies the assumptions of an instrumental variable. To provide some protection against failure of these assumptions, robust methods for instrumental variable analysis have been proposed. Here, we develop three extensions to instrumental variable methods using: i) robust regression, ii) the penalization of weights from candidate instruments with heterogeneous causal estimates, and iii) L1 penalization. Results from a wide variety of robust methods, including the recently-proposed MR-Egger and median-based methods, are compared in an extensive simulation study. We demonstrate that two methods, robust regression in an inverse-variance weighted method and a simple median of the causal estimates from the individual variants, have considerably improved Type 1 error rates compared with conventional methods in a wide variety of scenarios when up to 30% of the genetic variants are invalid instruments. While the MR-Egger method gives unbiased estimates when its assumptions are satisfied, these estimates are less efficient than those from other methods and are highly sensitive to violations of the assumptions. Methods that make different assumptions should be used routinely to assess the robustness of findings from applied Mendelian randomization investigations with multiple genetic variants.