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Pairwise Growth Competition Assay for Determining the Replication Fitness of Human Immunodeficiency Viruses
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Efficient odds ratio estimation under two-phase sampling using error-prone data from a multi-national HIV research

Sarah C Lotspeich1, Bryan E Shepherd1, Gustavo G C Amorim1

  • 1Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

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|July 2, 2021
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Summary

This study introduces a new statistical method to accurately analyze HIV data from clinical cohorts, even when the data contains errors. The approach improves research reliability by efficiently using partially audited information.

Keywords:
case-control samplingdata auditselectronic health recordsmeasurement errormissing datasemiparametric efficiency

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Area of Science:

  • Biostatistics
  • Epidemiology
  • HIV Research

Background:

  • Observational HIV cohorts generate extensive clinical data.
  • Error-prone data in HIV research can lead to biased results.
  • Two-phase designs offer a cost-effective solution for data auditing.

Purpose of the Study:

  • To develop an efficient statistical method for odds ratio estimation using partially audited, error-prone data from HIV cohorts.
  • To accommodate complex error mechanisms, including correlated errors in outcomes and covariates.
  • To enable Phase II sample selection based on Phase I data.

Main Methods:

  • Proposed a semiparametric approach utilizing all data from Phase I and Phase II.
  • Developed a computationally efficient and numerically stable Expectation-Maximization (EM) algorithm.
  • The method allows for arbitrary dependence of Phase II selection on Phase I data.

Main Results:

  • The proposed method yields consistent, asymptotically normal, and asymptotically efficient estimators.
  • Extensive simulations demonstrated the advantages over existing methods.
  • The approach was applied to data from the Caribbean, Central, and South America network for HIV epidemiology (CCASAnet) cohort.

Conclusions:

  • The novel semiparametric approach provides reliable odds ratio estimation from partially audited, error-prone HIV data.
  • The efficient EM algorithm ensures computational feasibility and numerical stability.
  • This method enhances the quality and validity of biomedical research using real-world HIV cohort data.