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Multiple Imputation to Deal with Missing Clinical Data in Rheumatologic Surveys: an Application in the WHO-ILAR
M Mirmohammadkhani1, A Rahimi Foroushani, F Davatchi
1Dept. of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Iranian Journal of Public Health
|November 1, 2012
Summary
Multiple imputation (MI) significantly reduces bias in estimating rheumatic disease prevalence compared to complete case analysis (CCA). This method improves accuracy for rheumatologic surveys with missing data.
Area of Science:
- Epidemiology
- Biostatistics
- Rheumatology
Background:
- Missing clinical data is a challenge in rheumatologic surveys.
- The Community Oriented Program for Control of Rheumatic Disorders (COPCORD) in Iran provides a relevant dataset.
- Handling missing data is crucial for accurate prevalence estimation.
Purpose of the Study:
- To demonstrate the application of multiple imputation (MI) for managing missing clinical data in rheumatologic surveys.
- To compare the effectiveness of MI against complete case analysis (CCA) in handling missing data.
Main Methods:
- A dataset of 10,291 participants from the COPCORD study in Iran was used.
- Missing data patterns were simulated for knee osteoarthritis status.
- Complete case analysis (CCA) and multiple imputation (MI) with varying imputation numbers were applied.
- Analysis of variance was used to compare the two methods.
Main Results:
- Complete case analysis (CCA) resulted in a 8.67% bias for disease proportion and 13.67% bias for standard error.
- Multiple imputation (MI) with M=15 reduced bias to 6.42% for proportion and 10.04% for standard error.
- The reduction in percent bias using MI was statistically significant compared to CCA (P<0.05).
Conclusions:
- Multiple imputation (MI) is a superior method for estimating the prevalence of rheumatic disorders, including knee osteoarthritis.
- MI, utilizing available demographic data, provides more accurate estimates than complete case analysis (CCA) when dealing with missing data in rheumatologic studies.
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