The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Multiple Imputation With Large Data Sets: A Case Study of the Children's Mental Health Initiative
Open accessAbstract
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.
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Citations
Cites
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory
Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007) · Prevention Science · doi:10.1007/s11121-007-0070-9 · Cited by 2,795 · cited 1×
“Generally, 5–10 imputations are created, resulting in 5–10 ‘‘complete’’ data sets, although recent work has indicated that more imputations may be beneficial (21).”
A critical look at methods for handling missing covariates in epidemiologic regression analyses.
Greenland, S., & Finkle, W. D. (1995) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a117592 · Cited by 891 · cited 2×
“Doing so results in correct standard error estimates and coverage rates, as compared with single imputation methods or simply including a missing data indicator for each variable in the model (17, 18).”
Dealing with missing data in a multi-question depression scale: a comparison of imputation methods
Shrive, F. M., Stuart, H., Quan, H., & Ghali, W. A. (2006) · BMC Medical Research Methodology · doi:10.1186/1471-2288-6-57 · Cited by 650
Cited by
Multiple imputation by chained equations: what is it and how does it work?
Azur, M. J., Stuart, E. A., Frangakis, C., & Leaf, P. J. (2011) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.329 · Cited by 3,542
Self-citationTwisk, J., de Boer, M., de Vente, W., & Heymans, M. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.03.017 · Cited by 568
Bair-Merritt, M. H., Jennings, J. M., Chen, R., Burrell, L., McFarlane, E., Fuddy, L., & Duggan, A. K. (2010) · Archives of Pediatrics & Adolescent Medicine · doi:10.1001/archpediatrics.2009.237 · Cited by 117
Imputation approaches for potential outcomes in causal inference
Westreich, D., Edwards, J. K., Cole, S. R., Platt, R. W., Mumford, S. L., & Schisterman, E. F. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv135 · Cited by 64
A multiple imputation strategy for sequential multiple assignment randomized trials
Shortreed, S. M., Laber, E., Scott Stroup, T., Pineau, J., & Murphy, S. A. (2014) · Statistics in Medicine · doi:10.1002/sim.6223 · Cited by 61
Metadata
- Authors
- Stuart, E. A., Azur, M., Frangakis, C., & Leaf, P.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-03-24
- Volume
- 169
- Issue
- 9
- Pages
- 1133-1139
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W2168089684
- Cited by
- 268
- References
- 6
- FWCI
- 7.65
- Type
- journal article
- Language
- en
- Open access
- green
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