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Challenges and strategies in analysis of missing data

Challenges and strategies in analysis of missing data In biomedical research, missing data are a common problem. The statistical literature to solve this problem is well developed but overly technical and complicated for health science researchers who are not experts in statistics or methodology. In this paper, we review available statistical methods for handling missing data and provide health science researchers with the means of understanding the importance of missing data in their own personal research, and the ability to use these methods given the available software. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Challenges and strategies in analysis of missing data

Zhou, Xiao-Hua
Biostatistics & Epidemiology , Volume 4 (1): 9 – Jan 1, 2020
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Challenges and strategies in analysis of missing data

Abstract

In biomedical research, missing data are a common problem. The statistical literature to solve this problem is well developed but overly technical and complicated for health science researchers who are not experts in statistics or methodology. In this paper, we review available statistical methods for handling missing data and provide health science researchers with the means of understanding the importance of missing data in their own personal research, and the ability to use these methods...
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Publisher
Taylor & Francis
Copyright
© 2019 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2018.1469810
Publisher site
See Article on Publisher Site

Abstract

In biomedical research, missing data are a common problem. The statistical literature to solve this problem is well developed but overly technical and complicated for health science researchers who are not experts in statistics or methodology. In this paper, we review available statistical methods for handling missing data and provide health science researchers with the means of understanding the importance of missing data in their own personal research, and the ability to use these methods given the available software.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jan 1, 2020

Keywords: Missing data; maximum likelihood method; inverse probability weighting; missing data mechanism; multiple imputation

References

  • The prevention and treatment of missing data in clinical trials
  • Missing observations in multivariate statistics I: review of the literature
  • Maximum likelihood from incomplete data via the EM algorithm (with discussion)
  • Ignorability and coarse data
  • Semiparametric efficiency in multivariate regression models with missing data
  • Missing data: our view of the state of the art
  • Mice: multivariate imputation by chained equations in R
  • A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms
  • Maximum likelihood estimation for spatial models by Markov Chain Monte Carlo stochastic approximation
  • Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM