Using MICE to investigate loss to follow up in a 10 year cohort of HIV positive patients in Haiti

Loss to follow-up is unavoidable in many public health studies. Tracing all subjects may be impractical or prohibitively expensive. Traditional methods, including Kaplan-Meier analysis and inverse probability weighting (IPW), produce biased estimates if loss is not independent of survival. Multiple imputation with chained equations (MICE) provides an acceptable, robust and cost saving solution to this problem for HIV research in developing countries with limited resources. To illustrate utility, we applied MICE to ascertain outcome status of people who were lost to follow up within a cohort of N=910 HIV positive people followed for ten years in Port au Prince Haiti, 17% (n = 156) were lost to follow-up and 8% (n = 71) transferred facilities. Contact tracing was performed and 45 of the 156 subjects identified as lost to follow-up were found; 37 alive and 8 deceased. Analysis using IPW based on the traced subjects predicted that 63% of all subjects were alive at 10 years (95% CI 0.59-0.67). Results from MICE predicted that within 6 months 12% (95% CI 0.86-0.90) of those who were lost to follow-up or transferred were dead and 88% were alive (95% CI 0.10-0.14). At 10 years, 33% were predicted to be dead (95% CI 0.29-0.36) and 67% (95% CI: 0.64-0.71) were predicted to be alive. We found MICE to be more robust in predicting status as it allowed us to impute missing data so that we had the maximum number of observations to perform regression analyses. Additionally, the results were easier to interpret, less likely to be biased, and provided an interesting insight into a problem that is often commented upon in the extant literature. Overall MICE is a useful cost saving method for studying survival compared to contact tracing for HIV research in developing countries.