Eleanor Sanderson | Searcharxiv

arXiv2026

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

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.

stat.ME↗

arXiv2022

Selecting Valid Instrumental Variables in Linear Models with Multiple Exposure Variables: Adaptive Lasso and the Median-of-Medians Estimator

In a linear instrumental variables (IV) setting for estimating the causal effects of multiple confounded exposure/treatment variables on an outcome, we investigate the adaptive Lasso method for selecting valid instrumental variables from a set of available instruments that may contain invalid ones. An instrument is invalid if it fails the exclusion conditions and enters the model as an explanatory variable. We extend the results as developed in Windmeijer et al. (2019) for the single exposure model to the multiple exposures case. In particular we propose a median-of-medians estimator and show that the conditions on the minimum number of valid instruments under which this estimator is consistent for the causal effects are only moderately stronger than the simple majority rule that applies to the median estimator for the single exposure case. The adaptive Lasso method which uses the initial median-of-medians estimator for the penalty weights achieves consistent selection with oracle properties of the resulting IV estimator. This is confirmed by some Monte Carlo simulation results. We apply the method to estimate the causal effects of educational attainment and cognitive ability on body mass index (BMI) in a Mendelian Randomization setting.

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