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.
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The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.
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No potential conflict of interest was reported by the author.
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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.