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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

Biostatistics & Epidemiology , Volume 4 (1): 9 – Jan 1, 2020

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 & EpidemiologyTaylor & Francis

Published: Jan 1, 2020

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

References