ArticleEpidemiology (Cambridge, Mass.)2015
Simultaneous Treatment of Missing Data and Measurement Error in HIV Research Using Multiple Overimputation.
Article in Epidemiology (Cambridge, Mass.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Recoverability of causal effects under presence of missing data: a longitudinal case study.Biostatistics (Oxford, England) · 2025Article
- Addressing Measurement Error in Intimate Partner Violence Self-report Data Using Multiple Overimputation and Multidimensional Quantitative Bias Analysis.Epidemiology (Cambridge, Mass.) · 2025Article
- Bootstrap inference when using multiple imputation.Statistics in medicine · 2018Article
- Estimation of measurement error in plasma HIV-1 RNA assays near their limit of quantification.PloS one · 2017Article
- Multiple Overimputation to Address Missing Data and Measurement Error: Application to HIV Treatment During Pregnancy and Pregnancy Outcomes.Epidemiology (Cambridge, Mass.) · 2016Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundBoth CD4 count and viral load in HIV-infected persons are measured with error. There is no clear guidance on how to deal with this measurement error in the presence of missing data.
methodsWe used multiple overimputation, a method recently developed in the political sciences, to account for both measurement error and missing data in CD4 count and viral load measurements from four South African cohorts of a Southern African HIV cohort collaboration. Our knowledge about the measurement error of ln CD4 and log10 viral load is part of an imputation model that imputes both missing and mismeasured data. In an illustrative example, we estimate the association of CD4 count and viral load with the hazard of death among patients on highly active antiretroviral therapy by means of a Cox model. Simulation studies evaluate the extent to which multiple overimputation is able to reduce bias in survival analyses.
resultsMultiple overimputation emphasizes more strongly the influence of having high baseline CD4 counts compared to both a complete case analysis and multiple imputation (hazard ratio for >200 cells/mm vs. <25 cells/mm: 0.21 [95% confidence interval: 0.18, 0.24] vs. 0.38 [0.29, 0.48], and 0.29 [0.25, 0.34], respectively). Similar results are obtained when varying assumptions about measurement error, when using p-splines, and when evaluating time-updated CD4 count in a longitudinal analysis. The estimates of the association with viral load are slightly more attenuated when using multiple imputation instead of multiple overimputation. Our simulation studies suggest that multiple overimputation is able to reduce bias and mean squared error in survival analyses.
conclusionsMultiple overimputation, which can be used with existing software, offers a convenient approach to account for both missing and mismeasured data in HIV research.
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