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Treating loss-to-follow-up as a missing data problem: a case study using a longitudinal cohort of HIV-infected
Deanna P Jannat-Khah1, Michelle Unterbrink2, Margaret McNairy2,3
1Division of General Internal Medicine, Department of Medicine, Weill Cornell Medical College, 1300 York Avenue, New York, NY, USA. Dej2008@med.cornell.edu.
Insights
Estimating survival in HIV patients lost to follow-up (LTF) is crucial. Multiple Imputation with Chained Equations (MICE) proved most robust for predicting vital status, offering a less biased alternative for survival analysis.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- HIV patient retention and vital status are key program metrics.
- Lost to follow-up (LTF) patients have unknown vital status, impacting survival analyses.
- Accurate estimation of mortality among LTF patients is critical for program evaluation.
Purpose of the Study:
- To estimate survival and identify predictors of death in HIV-positive patients receiving antiretroviral therapy in Haiti.
- To compare the performance of three methods (complete case, IPW, MICE) for handling missing vital status data.
Main Methods:
- Employed complete case (CC), Inverse Probability Weights (IPW), and Multiple Imputation with Chained Equations (MICE) to estimate survival.
- Used logistic regression to identify factors associated with death at 10 years.
- Controlled for demographic and clinical variables including age, poverty, and WHO stage.
Main Results:
- Age, severe poverty, baseline weight, and WHO stage significantly predicted mortality across all models.
- Gender was a significant predictor in the MICE model and showed a notable difference in odds ratios across models.
Conclusions:
- Multiple Imputation with Chained Equations (MICE) is a robust method for imputing missing data and maximizing observations for survival analysis.
- MICE offers a less biased and more interpretable alternative for estimating survival among patients with unassigned vital status.
- This approach addresses a common challenge in HIV survival literature and improves program evaluation accuracy.
Background:
HIV programs are often assessed by the proportion of patients who are alive and retained in care; however some patients are categorized as lost to follow-up (LTF) and have unknown vital status. LTF is not an outcome but a mixed category of patients who have undocumented death, transfer and disengagement from care. Estimating vital status (dead versus alive) among this category is critical for survival analyses and program evaluation.
Methods:
We used three methods to estimate survival in the cohort and to ascertain factors associated with death among the first cohort of HIV positive patients to receive antiretroviral therapy in Haiti: complete case (CC) (drops missing), Inverse Probability Weights (IPW) (uses tracking data) and Multiple Imputation with Chained Equations (MICE) (imputes missing data). Logistic regression was used to calculate odds ratios and 95% confidence intervals for adjusted models for death at 10 years. The logistic regression models controlled for sex, age, severe poverty (living on <$1 USD per day), Port-au-Prince residence and baseline clinical characteristics of weight, CD4, WHO stage and tuberculosis diagnosis.
Results:
Age, severe poverty, baseline weight and WHO stage were statistically significant predictors of AIDS related mortality across all models. Gender was only statistically significant in the MICE model but had at least a 10% difference in odds ratios across all models.
Conclusion:
Each of these methods had different assumptions and differed in the number of observations included due to how missing values were addressed. We found MICE to be most robust in predicting survival status as it allowed us to impute missing data so that we had the maximum number of observations to perform regression analyses. MICE also provides a complementary alternative for estimating survival among patients with unassigned vital status. Additionally, the results were easier to interpret, less likely to be biased and provided an alternative to a problem that is often commented upon in the extant literature.
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