Multiple Imputation to Deal with Missing Clinical Data in Rheumatologic Surveys: an Application in the WHO-ILAR COPCORD Study in Iran

BACKGROUND: The aim of the article is demonstrating an application of multiple imputation (MI) for handling missing clinical data in the setting of rheumatologic surveys using data derived from 10291 people participating in the first phase of the Community Oriented Program for Control of Rheumatic Disorders (COPCORD) in Iran. METHODS: Five data subsets were produced from the original data set. Certain demographics were selected as complete variables. In each subset, we created a univariate pattern of missingness for knee osteoarthritis status as the outcome variable (disease) using different mechanisms and percentages. The crude disease proportion and its standard error were estimated separately for each complete data set to be used as true (baseline) values for percent bias calculation. The parameters of interest were also estimated for each incomplete data subset using two approaches to deal with missing data including complete case analysis (CCA) and MI with various imputation numbers. The two approaches were compared using appropriate analysis of variance. RESULTS: With CCA, percent bias associated with missing data was 8.67 (95% CI: 7.81–9.53) for the proportion and 13.67 (95% CI: 12.60–14.74) for the standard error. However, they were 6.42 (95% CI: 5.56–7.29) and 10.04 (95% CI: 8.97–11.11), respectively using the MI method (M=15). Percent bias in estimating disease proportion and its standard error was significantly lower in missing data analysis using MI compared with CCA (P< 0.05). CONCLUSION: To estimate the prevalence of rheumatic disorders such as knee osteoarthritis, applying MI using available demographics is superior to CCA.