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Estimating HIV Cross-sectional Incidence Using Recency Tests from a Non-representative Sample.

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This summary is machine-generated.

Estimating HIV incidence using recency testing is challenged by non-representative sampling. Excluding recently tested individuals is crucial for reliable HIV prevention trial data, but requires balancing bias and variance.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Cross-sectional incidence estimation using recency testing is standard in HIV research.
  • This method estimates placebo incidence in HIV prevention trials using screening data.
  • Non-representative sampling poses challenges, as HIV-positive individuals may avoid screening.

Purpose of the Study:

  • To develop a statistical framework for recency-based incidence estimation under non-representative sampling.
  • To evaluate the validity of excluding recently tested individuals in HIV prevention trials.
  • To quantify estimation errors and investigate the performance of incidence estimators.

Main Methods:

  • Developed a statistical framework incorporating non-representative sampling and a testing-based exclusion criterion.
  • Quantified limiting estimation error in recency-based incidence estimates.
  • Conducted simulations emulating real-world trial designs to assess estimator performance.

Main Results:

  • The incidence estimator is unreliable under non-representative sampling without excluding recently tested individuals.
  • Excluding recently tested individuals is necessary for accurate HIV incidence estimation.
  • A bias-variance trade-off exists: increased exclusion reduces bias but raises variance.

Conclusions:

  • The testing-based criterion's validity needs careful consideration in HIV prevention trials.
  • Improved methods are essential for reliable cross-sectional HIV incidence estimation.
  • Balancing bias and variance is key when applying exclusion criteria.