Valid instrumental variable selection method using negative control outcomes and constructing efficient estimator.

Autor: Orihara S; Department of Health Data Science, Tokyo Medical University, Tokyo, Japan.; Graduate School of Data Science, Yokohama City University, Kanagawa, Japan., Goto A; Graduate School of Data Science, Yokohama City University, Kanagawa, Japan., Taguri M; Department of Health Data Science, Tokyo Medical University, Tokyo, Japan.; Graduate School of Data Science, Yokohama City University, Kanagawa, Japan.
Jazyk: angličtina
Zdroj: Biometrical journal. Biometrische Zeitschrift [Biom J] 2024 Jun; Vol. 66 (4), pp. e2300113.
DOI: 10.1002/bimj.202300113
Abstrakt: In observational studies, instrumental variable (IV) methods are commonly applied when there are unmeasured covariates. In Mendelian randomization, constructing an allele score using many single nucleotide polymorphisms is often implemented; however, estimating biased causal effects by including some invalid IVs poses some risks. Invalid IVs are those IV candidates that are associated with unobserved variables. To solve this problem, we developed a novel strategy using negative control outcomes (NCOs) as auxiliary variables. Using NCOs, we are able to select only valid IVs and exclude invalid IVs without knowing which of the instruments are invalid. We also developed a new two-step estimation procedure and proved the semiparametric efficiency of our estimator. The performance of our proposed method was superior to some previous methods through simulations. Subsequently, we applied the proposed method to the UK Biobank dataset. Our results demonstrate that the use of an auxiliary variable, such as an NCO, enables the selection of valid IVs with assumptions different from those used in previous methods.
(© 2024 Wiley‐VCH GmbH.)
Databáze: MEDLINE
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