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American Journal of Epidemiology Advance Access originally published online on March 24, 2009
American Journal of Epidemiology 2009 169(9):1133-1139; doi:10.1093/aje/kwp026
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American Journal of Epidemiology © The Author 2009. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

PRACTICE OF EPIDEMIOLOGY

Multiple Imputation With Large Data Sets: A Case Study of the Children's Mental Health Initiative

Elizabeth A. Stuart, Melissa Azur, Constantine Frangakis and Philip Leaf

Correspondence to Dr. Elizabeth A. Stuart, Johns Hopkins Bloomberg School of Public Health, 624 North Broadway, 8th Floor, Baltimore, MD 21205 (e-mail: estuart{at}jhsph.edu).

Received for publication August 4, 2008. Accepted for publication January 21, 2009.

Multiple imputation is an effective method for dealing with missing data, and it is becoming increasingly common in many fields. However, the method is still relatively rarely used in epidemiology, perhaps in part because relatively few studies have looked at practical questions about how to implement multiple imputation in large data sets used for diverse purposes. This paper addresses this gap by focusing on the practicalities and diagnostics for multiple imputation in large data sets. It primarily discusses the method of multiple imputation by chained equations, which iterates through the data, imputing one variable at a time conditional on the others. Illustrative data were derived from 9,186 youths participating in the national evaluation of the Community Mental Health Services for Children and Their Families Program, a US federally funded program designed to develop and enhance community-based systems of care to meet the needs of children with serious emotional disturbances and their families. Multiple imputation was used to ensure that data analysis samples reflect the full population of youth participating in this program. This case study provides an illustration to assist researchers in implementing multiple imputation in their own data.

mental health services; missing at random; missing data; multiple imputation

Abbreviations: CMHI, Children's Mental Health Initiative; MAR, missing at random; MCAR, missing completely at random; MICE, multiple imputation by chained equations; NMAR, not missing at random


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