The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Imputations of Missing Values in Practice: Results from Imputations of Serum Cholesterol in 28 Cohort Studies
Abstract
Missing values, common in epidemiologic studies, are a major issue in obtaining valid estimates. Simulation studies have suggested that multiple imputation is an attractive method for imputing missing values, but it is relatively complex and requires specialized software. For each of 28 studies in the Asia Pacific Cohort Studies Collaboration, a comparison of eight imputation procedures (unconditional and conditional mean, multiple hot deck, expectation maximization, and four different approaches to multiple imputation) and the naive, complete participant analysis are presented in this paper. Criteria used for comparison were the mean and standard deviation of total cholesterol and the estimated coronary mortality hazard ratio for a one-unit increase in cholesterol. Further sensitivity analyses allowed for systematic over- or underestimation of cholesterol. For 22 studies for which less than 10% of the values for cholesterol were missing, and for the pooled Asia Pacific Cohort Studies Collaboration, all methods gave similar results. For studies with roughly 10-60% missing values, clear differences existed between the methods, in which case past research suggests that multiple imputation is the method of choice. For two studies with over 60% missing values, no imputation method seemed to be satisfactory.
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Cites
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Cited by
Multiple imputation using chained equations: Issues and guidance for practice
White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792
A Review of Hot Deck Imputation for Survey Non‐response
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Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes
Ninomiya, T., Perkovic, V., de Galan, B. E., Zoungas, S., Pillai, A., Jardine, M., Patel, A., Cass, A., Neal, B., Poulter, N., et al. (2009) · Journal of the American Society of Nephrology · doi:10.1681/asn.2008121270 · Cited by 965
Imputing missing covariate values for the Cox model
White, I. R., & Royston, P. (2009) · Statistics in Medicine · doi:10.1002/sim.3618 · Cited by 985
Registries for Evaluating Patient Outcomes: A User’s Guide
(2020) · doi:10.23970/ahrqepcregistries4 · Cited by 960
Metadata
- Authors
- Barzi, F.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-07-01
- Volume
- 160
- Issue
- 1
- Pages
- 34-45
- ISSN
- 0002-9262 (ISSN-L)
- OpenAlex
- W2109927751
- Cited by
- 261
- References
- 23
- FWCI
- 8.86
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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