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.
Related
- BGM-IV: AI-Powered Bayesian Generative Modeling for Instrumental Variable Regression with High-Dimensional Covariates
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- Identifying Causal Effects in Experiments with Latent Treatments: An Instrumental Variable Approach
- Sharp Instrumental Variable Bounds under The Marginal Sensitivity Model
BibTeX
@misc{palmer2026onesample,
title = {{One-Sample Mendelian Randomization and Instrumental Variable Analyses in R and Stata: The OneSampleMR and ivonesamplemr Packages}},
author = {Tom Palmer and Nicolai Vitt and Eleanor Sanderson},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.02887},
eprint = {2611.02887},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.02887},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - One-Sample Mendelian Randomization and Instrumental Variable Analyses in R and Stata: The OneSampleMR and ivonesamplemr Packages
AU - Tom Palmer
AU - Nicolai Vitt
AU - Eleanor Sanderson
PY - 2026
DA - 2026/11/02
PB - arXiv
DO - 10.48550/arxiv.2611.02887
UR - https://arxiv.org/abs/2611.02887
AB - 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.
ER -