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JAMA Guide to Statistics and Methods |

Multiple Imputation A Flexible Tool for Handling Missing Data

Peng Li, PhD1,2; Elizabeth A. Stuart, PhD3; David B. Allison, PhD1,2
[+] Author Affiliations
1Office of Energetics and Nutrition Obesity Research Center, University of Alabama at Birmingham
2Department of Biostatistics, School of Public Health, University of Alabama at Birmingham
3Departments of Mental Health, Biostatistics, and Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
JAMA. 2015;314(18):1966-1967. doi:10.1001/jama.2015.15281.
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This Guide to Statistics and Methods discusses the use of multiple imputation in statistical analyses when data are missing for some participants in a clinical trial.

In this issue of JAMA, Asch et al1 report results of a cluster randomized clinical trial designed to evaluate the effects of physician financial incentives, patient incentives, or shared physician and patient incentives on low-density lipoprotein cholesterol (LDL-C) levels among patients with high cardiovascular risk. Because 1 or more follow-up LDL-C measurements were missing for approximately 7% of participants, Asch et al used multiple imputation (MI) to analyze their data and concluded that shared financial incentives for physicians and patients, but not incentives to physicians or patients alone, resulted in the patients having lower LDL-C levels. Imputation is the process of replacing missing data with 1 or more specific values, to allow statistical analysis that includes all participants and not just those who do not have any missing data.

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