A data-adaptive method for investigating effect heterogeneity with high-dimensional covariates in Mendelian randomization.

ABSTRACT:

Background

Mendelian randomization is a popular method for causal inference with observational data that uses genetic variants as instrumental variables. Similarly to a randomized trial, a standard Mendelian randomization analysis estimates the population-averaged effect of an exposure on an outcome. Dividing the population into subgroups can reveal effect heterogeneity to inform who would most benefit from intervention on the exposure. However, as covariates are measured post-"randomization", naive stratification typically induces collider bias in stratum-specific estimates.

Method

We extend a previously proposed stratification method (the "doubly-ranked method") to form strata based on a single covariate, and introduce a data-adaptive random forest method to calculate stra

SUBMITTER: Tian H 

PROVIDER: S-EPMC10858611 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature