Journal Article

Imputations of Missing Values in Practice: Results from Imputations of Serum Cholesterol in 28 Cohort Studies

Federica Barzi and Mark Woodward

in American Journal of Epidemiology

Published on behalf of Johns Hopkins Bloomberg School of Public Health

Volume 160, issue 1, pages 34-45
Published in print July 2004 | ISSN: 0002-9262
Published online July 2004 | e-ISSN: 1476-6256 | DOI: http://dx.doi.org/10.1093/aje/kwh175
Imputations of Missing Values in Practice: Results from Imputations of Serum Cholesterol in 28 Cohort Studies

More Like This

Show all results sharing this subject:

  • Public Health and Epidemiology

GO

Show Summary Details

Preview

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.

Keywords: bias; cholesterol; coronary disease; hazard rate; imputation; meta-analysis; missing data; mortality; Abbreviations: APCSC, Asia Pacific Cohort Studies Collaboration; CHD, coronary heart disease; DBP, diastolic blood pressure; EM, expectation maximization; MI, multiple imputation; SBP, systolic blood pressure.

Journal Article.  5964 words.  Illustrated.

Subjects: Public Health and Epidemiology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.