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How should we best estimate the mean recency duration for the BED method?
John Hargrove1, Hayden Eastwood, Guy Mahiane
1The South African Department of Science and Technology/National Research Foundation Centre of Excellence in Epidemiological Modelling and Analysis, University of Stellenbosch, Stellenbosch, South Africa. jhargrove@sun.ac.za
Plos One
|November 21, 2012
Summary
Estimating HIV incidence using BED assay requires accurate mean recency duration (Ω(T)). Non-linear mixed modeling (NLMM) provided the best fit and most reliable estimates for this crucial parameter.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Biomarker-Enzyme-Detergent (BED) assay is used for estimating HIV incidence in cross-sectional surveys.
- Accurate estimation of mean recency duration (Ω(T)) is critical for reliable HIV incidence calculations.
- Various statistical methods exist for estimating Ω(T), each with potential limitations.
Purpose of the Study:
- To evaluate and compare five different methods for estimating the mean recency duration (Ω(T)) using BED assay data.
- To determine the most accurate and reliable method for Ω(T) estimation in the context of HIV incidence surveillance.
- To assess the impact of the chosen cut-off (C) on false-recent rates and incidence estimation.
Main Methods:
- The study compared five methods: ratio (r/s), linear mixed modeling (LMM), non-linear mixed modeling (NLMM), survival analysis (SA), and graphical analysis.
- Data from postpartum women in Zimbabwe were used to estimate Ω(T) for a cut-off C = 0.8.
- Model fits, variance of estimates, and correspondence with follow-up incidence data were used for comparison.
Main Results:
- All five methods provided similar estimates of Ω(T), ranging from 191 to 196 days.
- Non-linear mixed modeling (NLMM) demonstrated the best fit to optical density data and the smallest variance in Ω(T) estimates.
- NLMM showed the best correspondence between BED and follow-up HIV incidence estimates.
- Survival analysis (SA) and NLMM yielded similar Ω(T) estimates, but SA had a higher coefficient of variation.
- False-recent rates increased quadratically with the cut-off (C).
Conclusions:
- Non-linear mixed modeling (NLMM) is recommended as the preferred method for estimating mean recency duration (Ω(T)) with BED assay data due to its superior fit and reliability.
- Accurate estimation of Ω(T) is achievable and should not impede HIV incidence estimation.
- Choosing a small cut-off (C) is advisable to minimize false-recent rates while ensuring sufficient recent cases for accurate estimation.
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