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Statistics in Biosciences

, Volume 9, Issue 2, pp 320–338 | Cite as

Strengthening Instrumental Variables Through Weighting

  • Douglas LehmannEmail author
  • Yun Li
  • Rajiv Saran
  • Yi Li
Article

Abstract

Instrumental variable (IV) methods are widely used to deal with the issue of unmeasured confounding and are becoming popular in health and medical research. IV models are able to obtain consistent estimates in the presence of unmeasured confounding, but rely on assumptions that are hard to verify and often criticized. An instrument is a variable that influences or encourages individuals toward a particular treatment without directly affecting the outcome. Estimates obtained using instruments with a weak influence over the treatment are known to have larger small-sample bias and to be less robust to the critical IV assumption that the instrument is randomly assigned. In this work, we propose a weighting procedure for strengthening the instrument while matching. Through simulations, weighting is shown to strengthen the instrument and improve robustness of resulting estimates. Unlike existing methods, weighting is shown to increase instrument strength without compromising match quality. We illustrate the method in a study comparing mortality between kidney dialysis patients receiving hemodialysis or peritoneal dialysis as treatment for end-stage renal disease.

Keywords

Causal inference End-stage renal disease Instrumental variables Unmeasured confounding Weak instruments 

Notes

Acknowledgments

The authors have benefited from conversations with Michael Biaocchi. This project has been funded in whole or in part through funds from the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institutes of Health, and the Department of Health and Human Services, under Contract No. HHSN276201400001C. Yun Li is funded by the National Institutes of Health (R01-DK070869).

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Copyright information

© International Chinese Statistical Association 2016

Authors and Affiliations

  1. 1.Department of Biostatistics, School of Public HealthUniversity of MichiganAnn ArborUSA
  2. 2.Department of Nephrology, School of MedicineUniversity of MichiganAnn ArborUSA

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