ArticleAmerican journal of epidemiology2017
Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies.
Article in American journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Comparing the performance of two-stage residual inclusion methods when using physician's prescribing preference as an instrumental variable: unmeasured confounding and noncollapsibility.Journal of comparative effectiveness research · 2024Article
- Effect of supplementary private health insurance on out-of-pocket inpatient medical expenditure: evidence from Malaysia.Health policy and planning · 2024Article
- Effect of supplementary private health insurance on inpatient utilisation: Evidence from Malaysia.Heliyon · 2023Article
- Simultaneous Maximum Likelihood Estimation for Piecewise Linear Instrumental Variable Models.Entropy (Basel, Switzerland) · 2022Article
- Sex Differences in the Risk of Coronary Heart Disease Associated With Type 2 Diabetes: A Mendelian Randomization Analysis.Diabetes care · 2021Article
- Association Between Receipt of a Medically Tailored Meal Program and Health Care Use.JAMA internal medicine · 2019Article
- Combined Association of Body Mass Index and Alcohol Consumption With Biomarkers for Liver Injury and Incidence of Liver Disease: A Mendelian Randomization Study.JAMA network open · 2019Article
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Authors and funding
4 authors.
Funding
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Abstract
Mendelian randomization studies use genotypes as instrumental variables to test for and estimate the causal effects of modifiable risk factors on outcomes. Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing to make parametric assumptions. However, researchers are currently reporting uncorrected or heteroscedasticity-robust standard errors for these estimates. We compared several different forms of the standard error for linear and logistic TSRI estimates in simulations and in real-data examples. Among others, we consider standard errors modified from the approach of Newey (1987), Terza (2016), and bootstrapping. In our simulations Newey, Terza, bootstrap, and corrected 2-stage least squares (in the linear case) standard errors gave the best results in terms of coverage and type I error. In the real-data examples, the Newey standard errors were 0.5% and 2% larger than the unadjusted standard errors for the linear and logistic TSRI estimators, respectively. We show that TSRI estimators with modified standard errors have correct type I error under the null. Researchers should report TSRI estimates with modified standard errors instead of reporting unadjusted or heteroscedasticity-robust standard errors.
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