Comparison of variance estimators for meta-analysis of instrumental variable estimates.


Schmidt, AF; Hingorani, AD; Jefferis, BJ; White, J; Groenwold, RH; Dudbridge, F; UCLEB Consortium, ; (2016) Comparison of variance estimators for meta-analysis of instrumental variable estimates. International journal of epidemiology. ISSN 0300-5771 DOI: https://doi.org/10.1093/ije/dyw123

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Abstract

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. 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. 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. 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.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology
Research Centre: Centre for Statistical Methodology
Related URLs:
PubMed ID: 27591262
Web of Science ID: 398261100035
URI: http://researchonline.lshtm.ac.uk/id/eprint/2837720

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