Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies

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...

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Bibliographic Details
Published in:American journal of epidemiology 2017-11, Vol.186 (9), p.1104-1114
Main Authors: Palmer, Tom M, Holmes, Michael V, Keating, Brendan J, Sheehan, Nuala A
Format: Article
Language:English
Subjects:
Bias
Body Mass Index
Causality
Computer Simulation
Confounding Factors (Epidemiology)
Data Interpretation, Statistical
Diabetes Mellitus - epidemiology
Diabetes Mellitus - etiology
Diabetes Mellitus - genetics
Genetic Predisposition to Disease
Genotype
Humans
Hypertension - epidemiology
Hypertension - etiology
Hypertension - genetics
Least-Squares Analysis
Linear Models
Logistic Models
Mendelian Randomization Analysis
Practice of Epidemiology
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Summary: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.
ISSN:0002-9262
1476-6256
DOI:10.1093/aje/kwx175