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.

BMC Public Health
|November 21, 2018
PubMed

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.
Abstract

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