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March 2021 Robust inference when combining inverse-probability weighting and multiple imputation to address missing data with application to an electronic health records-based study of bariatric surgery
Tanayott Thaweethai, David E. Arterburn, Karen J. Coleman, Sebastien Haneuse
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Ann. Appl. Stat. 15(1): 126-147 (March 2021). DOI: 10.1214/20-AOAS1386

Abstract

While electronic health records present a rich and promising data source for observational research, they are highly susceptible to missing data. For settings like these, Seaman et al. (Biometrics 68 (2012) 129–137) proposed a strategy wherein one handles missingness in some variables using inverse-probability weighting and others using multiple imputation. Seaman et al. (Biometrics 68 (2012) 129–137) show that Rubin’s variance estimator for averaging results across datasets is asymptotically valid when the analysis and imputation models are correctly specified and the weights are either known or correctly specified. Modeled after the approach of Robins and Wang (Biometrika 87 (2000) 113–124), we propose a method for asymptotically valid inference that is robust to violation of these conditions. Following a simulation study in which we demonstrate that a proposed variance estimator can reduce bias due to model misspecification, we illustrate this approach in an electronic health records-based study investigating whether differences in long-term weight loss between bariatric surgery techniques are associated with chronic kidney disease at baseline. We observe that the weight loss advantage after five years of Roux-en-Y gastric bypass surgery, compared to vertical sleeve gastrectomy, is less pronounced among patients with chronic kidney disease at baseline compared to those without.

Acknowledgments

We would like to thank the Editor, Associate Editor and referees for their insightful feedback regarding this manuscript which resulted in meaningful improvements in both the methodology and the data application. We would also like to thank Lisa J. Herrinton, a principal investigator of the DURABLE study, for providing data from Kaiser Permanente Northern California. T.T. is also affiliated with the Department of Medicine at Harvard Medical School and was supported by NIH/NIDDK Award Number F-31 DK118817. D.E.A., K.J.C. and S.H. were supported by NIH/NIDDK Award Number R-01 DK105960. S.H. was supported by NIH/NCI Award Number P-50 CA244433-01.

Citation

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Tanayott Thaweethai. David E. Arterburn. Karen J. Coleman. Sebastien Haneuse. "Robust inference when combining inverse-probability weighting and multiple imputation to address missing data with application to an electronic health records-based study of bariatric surgery." Ann. Appl. Stat. 15 (1) 126 - 147, March 2021. https://doi.org/10.1214/20-AOAS1386

Information

Received: 1 February 2020; Revised: 1 August 2020; Published: March 2021
First available in Project Euclid: 18 March 2021

Digital Object Identifier: 10.1214/20-AOAS1386

Rights: Copyright © 2021 Institute of Mathematical Statistics

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Vol.15 • No. 1 • March 2021
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