Journal
JOURNAL OF CLINICAL EPIDEMIOLOGY
Volume 63, Issue 7, Pages 728-736Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jclinepi.2009.08.028
Keywords
Missing; Confounder; Etiologic; Indicator method; Complete case; Multiple imputation
Funding
- Novo Nordisk
- Scientific Institute of the Dutch Pharmacists (WINAp)
- Netherlands Organization for Scientific Research, a VIDI grant [917-66-311]
- European Commission [PREDICT-QL4-CT2002-00683]
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Objective: Missing indicator method (MIM) and complete case analysis (CC) are frequently used to handle missing confounder data. Using empirical data, we demonstrated the degree and direction of bias in the effect estimate when using these methods compared with multiple imputation (MI). Study Design and Setting: From a cohort study, we selected an exposure (marital status), outcome (depression), and confounders (age, sex, and income). Missing values in income were created according to different patterns of missingness: missing values were created completely at random and depending on exposure and outcome values. Percentages of missing values ranged from 2.5% to 30%. Results: When missing values were completely random, MIM gave an overestimation of the odds ratio, whereas CC and MI gave unbiased results. MIM and CC gave under- or overestimations when missing values depended on observed values. Magnitude and direction of bias depended on how the missing values were related to exposure and outcome. Bias increased with increasing percentage of missing values. Conclusion: MIM should not be used in handling missing confounder data because it gives unpredictable bias of the odds ratio even with small percentages of missing values. CC can be used when missing values are completely random, but it gives loss of statistical power. (C) 2010 Elsevier Inc. All rights reserved.
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