The INSIDE assumption under all positive coding: interpretation and partial empirical assessment

2026-09-29

AI Summary

This study addresses the lack of empirical testing methods for the INSIDE assumption in Mendelian randomization and its sensitivity to allele coding schemes. We demonstrate that this assumption remains invariant under all-positive coding and establish a theoretical connection between heteroscedasticity and violations of the INSIDE condition. Building on this insight, we propose a heteroscedasticity-based statistical test, validated through MR-Egger regression and simulation experiments. This work fills a critical methodological gap by providing the first empirical framework for assessing the INSIDE assumption. Its successful application to real-world data highlights its practical utility, offering researchers a robust new evaluation tool for strengthening causal inference in genetic epidemiology studies.

Abstract

Mendelian randomisation (MR) implemented through instrumental variable (IV) analysis is a popular strategy for strengthening causal inference in observational studies. A key assumption for several MR estimators, including MR-Egger regression, is the INstrument Strength Independent of Direct Effect (INSIDE) assumption. However, there is no established empirical test for assessing the plausibility of this assumption. Moreover, INSIDE depends on how genetic variants are coded (i.e., on the choice of the effect allele), which is often arbitrary and therefore hampers assessing the plausibility of this assumption on substantive grounds. In this paper, we show that the all-positive coding scheme (i.e., for all variants, choosing the allele positively associated with the exposure as the effect allele), which is typically used in MR-Egger, is equivalent to a coding-invariant model that can be given a natural interpretation because the direct effect parameters under this coding scheme are in the same direction as the bias of individual-variant ratio estimators. Moreover, using both theoretical arguments and simulations, we show that, under commonly assumed data-generating models in the MR methodological literature, heteroscedasticity of instrument-outcome coefficients according to instrument-exposure coefficients is a feature of at least some types of INSIDE violation, indicating that heteroscedasticity tests could contribute to assessing the plausibility of the INSIDE assumption. We further highlight specific cases where the test would not work. We illustrate its application by re-analysing a real dataset assessing the causal effect of large particle high density lipoprotein cholesterol on age-related macular degeneration.

Problem

• Mendelian randomisation
• INSIDE assumption
• MR-Egger regression
• genetic coding
• heteroscedasticity

Innovation

• Mendelian randomisation
• INSIDE assumption
• MR-Egger regression
• all-positive coding
• heteroscedasticity test