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Comment & Response
February 2, 2016

Accounting for Missing Data in Clinical Research

Author Affiliations
  • 1Department of Mathematics and Statistics, Utah State University, Logan
  • 2BioStat Solutions Inc, Frederick, Maryland
  • 3Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
JAMA. 2016;315(5):517-518. doi:10.1001/jama.2015.16461

To the Editor Drs Newgard and Lewis1 provided an overview of many important issues to consider when dealing with missing data in clinical research. In particular, they summarized the following limitations of traditional imputation methods: failure to account for uncertainty in imputed values, failure to make full use of observed values, possibilities for bias, and artificially low variance.

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