One-Sample Mendelian Randomization and Instrumental Variable Analyses in R and Stata: The OneSampleMR and ivonesamplemr Packages

Abstract

Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to estimate the causal effect of an exposure on an outcome. When individual-level data on the instruments, exposure, and outcome are available in a single sample (one-sample MR), a range of IV estimators can be used beyond the standard two-stage least squares estimator, including structural mean models (SMMs) and two-stage predictor substitu- tion (TSPS) and two-stage residual inclusion (TSRI) estimators with nonlinear second stage models. These estimators are important when the outcome is binary, where in- vestigators typically wish to report causal risk ratios or causal odds ratios. However, implementations of these estimators are scattered across software packages and are of- ten unavailable with standard errors that account for the estimation of the first stage. We describe two packages which implement these estimators: OneSampleMR for R and ivonesamplemr for Stata. Both packages fit the multiplicative and logistic SMMs and TSPS and TSRI estimators with identity, log-additive, log-multiplicative, and logit links using the generalized method of moments (GMM), so that the standard errors account for the uncertainty in both stages of estimation. OneSampleMR additionally provides the Sanderson-Windmeijer conditional F-statistic for assessing instrument strength in models with multiple exposures, and ivonesamplemr additionally provides prefix commands for moving window and within-quantile estimation for exploring nonlinear exposure-outcome relationships. We illustrate both packages using the same simulated dataset and show that they give the same estimates as each other and as existing implementations.