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Extending balance assessment for the generalized propensity score under multiple imputation

  • Anna-Simone J. Frank ORCID logo EMAIL logo , David S. Matteson , Hiroko K. Solvang , Angela Lupattelli ORCID logo and Hedvig Nordeng ORCID logo
Published/Copyright: May 15, 2020
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Epidemiologic Methods
From the journal Epidemiologic Methods Volume 9 Issue 1

Abstract

This manuscript extends the definition of the Absolute Standardized Mean Difference (ASMD) for binary exposure (M = 2) to cases for M > 2 on multiple imputed data sets. The Maximal Maximized Standardized Difference (MMSD) and the Maximal Averaged Standardized Difference (MASD) were proposed. For different percentages, missing data were introduced in covariates in the simulated data based on the missing at random (MAR) assumption. We then investigate the performance of these two metric definitions using simulated data of full and imputed data sets. The performance of the MASD and the MMSD were validated by relating the balance metrics to estimation bias. The results show that there is an association between the balance metrics and bias. The proposed balance diagnostics seem therefore appropriate to assess balance for the generalized propensity score (GPS) under multiple imputation.

Keywords: balance diagnostics; generalized propensity score; missing data; Monte Carlo simulations; multiple treatment exposure
Corresponding author: Anna-Simone J. Frank, Computational Biology Unit, Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Bergen, Bergen, Norway, E-mail:

Funding source: United States Agency for International Development

Funding source: National Science Foundation

Award Identifier / Grant number: DMS-1455172

Funding source: New York State Division of Science, Technology and Innovation (NYSTAR)

Funding source: Cornell University Atkinson’s Center for a Sustainable Future (AVF-2017)

Funding source: Norwegian Women's Public Health Association

Funding source: Xerox PARC Faculty Research Award

Funding source: Cornell University Institute of Biotechnology

Funding source: UiO:Life Science internationalization support

Funding source: H2020 European Research Council

Award Identifier / Grant number: DrugsInPregnancy grant no. 639377

  1. Research funding: This research was funded by United States Agency for International Development, National Science Foundation (DMS-1455172), New York State Division of Science, Technology and Innovation (NYSTAR), Cornell University Atkinson's Center for a Sustainable Future (AVF-2017), Norwegian Women's Public Health Association, Xerox PARC Faculty Research Award, Cornell University Institute of Biotechnology, UiO:Life Science internationalization support, H2020 European Research Council (DrugsInPregnancy grant no. 639377).

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/em-2019-0003)

Received: 2019-01-30
Accepted: 2020-03-31
Published Online: 2020-05-15

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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  4. Heterogeneous indirect effects for multiple mediators using interventional effect models
  5. Sleep habits and their association with daytime sleepiness among medical students of Tanta University, Egypt
  6. Population attributable fractions for continuously distributed exposures
  7. A real-time search strategy for finding urban disease vector infestations
  8. Disease mapping models for data with weak spatial dependence or spatial discontinuities
  9. A comparison of cause-specific and competing risk models to assess risk factors for dementia
  10. A simple index of prediction accuracy in multiple regression analysis
  11. A comparison of approaches for estimating combined population attributable risks (PARs) for multiple risk factors
  12. Posterior predictive treatment assignment methods for causal inference in the context of time-varying treatments
  13. Random effects tumour growth models for identifying image markers of mammography screening sensitivity
  14. Extrapolating sparse gold standard cause of death designations to characterize broader catchment areas
  15. Extending balance assessment for the generalized propensity score under multiple imputation
  16. Regression analysis of unmeasured confounding
  17. The Use of Logic Regression in Epidemiologic Studies to Investigate Multiple Binary Exposures: An Example of Occupation History and Amyotrophic Lateral Sclerosis
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https://doi.org/10.1515/em-2019-0003
Keywords for this article
balance diagnostics; generalized propensity score; missing data; Monte Carlo simulations; multiple treatment exposure

Articles in the same Issue

  1. Editorial
  2. The mean prevalence
  3. Research Articles
  4. Heterogeneous indirect effects for multiple mediators using interventional effect models
  5. Sleep habits and their association with daytime sleepiness among medical students of Tanta University, Egypt
  6. Population attributable fractions for continuously distributed exposures
  7. A real-time search strategy for finding urban disease vector infestations
  8. Disease mapping models for data with weak spatial dependence or spatial discontinuities
  9. A comparison of cause-specific and competing risk models to assess risk factors for dementia
  10. A simple index of prediction accuracy in multiple regression analysis
  11. A comparison of approaches for estimating combined population attributable risks (PARs) for multiple risk factors
  12. Posterior predictive treatment assignment methods for causal inference in the context of time-varying treatments
  13. Random effects tumour growth models for identifying image markers of mammography screening sensitivity
  14. Extrapolating sparse gold standard cause of death designations to characterize broader catchment areas
  15. Extending balance assessment for the generalized propensity score under multiple imputation
  16. Regression analysis of unmeasured confounding
  17. The Use of Logic Regression in Epidemiologic Studies to Investigate Multiple Binary Exposures: An Example of Occupation History and Amyotrophic Lateral Sclerosis
  18. Meeting the Assumptions of Inverse-Intensity Weighting for Longitudinal Data Subject to Irregular Follow-Up: Suggestions for the Design and Analysis of Clinic-Based Cohort Studies
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