Comparison of variance estimators for meta-analysis of instrumental variable estimates.pdf (560.35 kB)
Comparison of variance estimators for meta-analysis of instrumental variable estimates
journal contributionposted on 2018-04-13, 14:06 authored by A. F. Schmidt, A. D. Hingorani, B. J. Jefferis, J. White, R. Groenwold, Frank Dudbridge, UCLEB Consortium
Background: Mendelian randomization studies perform instrumental variable (IV) analysis using genetic IVs. Results of individual Mendelian randomization studies can be pooled through meta-analysis. We explored how different variance estimators influence the meta-analysed IV estimate. Methods: Two versions of the delta method (IV before or after pooling), four bootstrap estimators, a jack-knife estimator and a heteroscedasticity-consistent (HC) variance estimator were compared using simulation. Two types of meta-analyses were compared, a two-stage meta-analysis pooling results, and a one-stage meta-analysis pooling datasets. Results: Using a two-stage meta-analysis, coverage of the point estimate using bootstrapped estimators deviated from nominal levels at weak instrument settings and/or outcome probabilities ≤ 0.10. The jack-knife estimator was the least biased resampling method, the HC estimator often failed at outcome probabilities ≤ 0.50 and overall the delta method estimators were the least biased. In the presence of between-study heterogeneity, the delta method before meta-analysis performed best. Using a one-stage meta-analysis all methods performed equally well and better than two-stage meta-analysis of greater or equal size. Conclusions: In the presence of between-study heterogeneity, two-stage meta-analyses should preferentially use the delta method before meta-analysis. Weak instrument bias can be reduced by performing a one-stage meta-analysis.
CitationInternational Journal of Epidemiology, 2016, 45 (6), pp. 1975-1986
Author affiliation/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Health Sciences
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