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Cross-Sectional HIV Incidence Estimation with Missing Biomarkers.

Doug Morrison1, Oliver Laeyendecker2, Jacob Konikoff3

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Statistical Communications in Infectious Diseases
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Summary

Accurate HIV incidence estimation using multi-assay algorithms (MAAs) requires handling missing biomarker data. A new conditional approach effectively prevents bias, unlike simpler methods, improving recent infection estimates from surveys.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Cross-sectional surveys with biomarkers of recent HIV infection are crucial for incidence estimation.
  • Multi-assay algorithms (MAAs) enhance precision by combining multiple biomarkers.
  • Missing biomarker data presents a significant statistical challenge in MAAs.

Purpose of the Study:

  • To address statistical challenges posed by missing biomarker data in multi-assay algorithms (MAAs).
  • To evaluate methods for estimating the mean window period and HIV incidence with missing data.
  • To compare a novel conditional estimation approach against two naive methods.

Main Methods:

  • Development of a conditional estimation approach for handling missing biomarker data.
  • Comparison of the conditional approach with two naive methods.
  • Simulation studies using MAAs for HIV subtype B to evaluate performance under missing data scenarios.

Main Results:

  • Naive methods for handling missing biomarker data resulted in biased HIV incidence estimates in most scenarios.
  • The proposed conditional estimation approach demonstrated robustness against bias across all evaluated missing data scenarios.
  • Simulation results confirmed the superiority of the conditional approach for accurate HIV incidence estimation.

Conclusions:

  • Missing biomarker data significantly impacts the accuracy of HIV incidence estimation using MAAs.
  • The developed conditional estimation approach effectively mitigates bias caused by missing data.
  • This method provides a reliable tool for precise HIV incidence estimation in cross-sectional surveys.