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 given the available software.

KEYWORDS:

• Missing data
• maximum likelihood method
• inverse probability weighting
• missing data mechanism
• multiple imputation

Acknowledgements

The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

Dr Zhou’s work was supported in part by U.S. Department of Veterans Affairs, Veterans Affairs Health Administration Research Career Scientist Award [RCS 05-196].

Notes on contributors

Xiao-Hua Zhou

Xiao-Hua Zhou Ph.D., is Boya Chair Professor in Beijing International Center for Mathematical Research and Chair of the Department of Biostatistics at Peking University. He is also a former Professor of the Department of Biostatistics at University of Washington and former Director of the Unit of Biostatistics at U.S. Department of Veterans Affairs Seattle Center of Innovation for Veteran-Centered and Value-Driven Care. He was awarded a Research Career Scientist Award by the U.S. Department of Veterans Affairs. He has made important contributions to medicine and public health by developing new statistical methods, particularly in diagnostic medicine and causal inference. Specifically, he has developed new statistical methods for (1) prediction and modeling of cost data, (2) causal inferences in broken clinical trials, such non-compliance and truncation by death, (3) studies on the accuracy of diagnostic tests, and (4) statistical methods for precision medicine. These statistical problems originate from collaborative research that he had been doing with his medical investigators. He has published over 245 statistical methodology and medical papers and is either the corresponding author or senior author on most of them; many of them have been published in top statistical journals, such as Journal of the Royal Statistical Society Series B (JRSS B), Journal of the American Statistical Association (JASA), Biometrics, Biometrika, Annals of Statistics, and Statistics in Medicine.