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Liu T, Hogan JW, Wang L, et al. Optimal Allocation of Gold Standard Testing under Constrained Availability: Application to Assessment of HIV Treatment Failure. J Am Stat Assoc. 2013;108(504):1173-1188doi: 10.1080/01621459.2013.810149.
Liu, T., Hogan, J. W., Wang, L., Zhang, S., & Kantor, R. (2013). Optimal Allocation of Gold Standard Testing under Constrained Availability: Application to Assessment of HIV Treatment Failure. Journal of the American Statistical Association, 108(504), 1173-1188. https://doi.org/10.1080/01621459.2013.810149
Liu, Tao, et al. "Optimal Allocation of Gold Standard Testing under Constrained Availability: Application to Assessment of HIV Treatment Failure." Journal of the American Statistical Association vol. 108,504 (2013): 1173-1188. doi: https://doi.org/10.1080/01621459.2013.810149
Liu T, Hogan JW, Wang L, Zhang S, Kantor R. Optimal Allocation of Gold Standard Testing under Constrained Availability: Application to Assessment of HIV Treatment Failure. J Am Stat Assoc. 2013 Jan 01;108(504):1173-1188. doi: 10.1080/01621459.2013.810149. PMID: 24672142; PMCID: PMC3963362.
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J Am Stat Assoc. 2013 Jan 01;108(504):1173-1188. doi: 10.1080/01621459.2013.810149.

Optimal Allocation of Gold Standard Testing under Constrained Availability: Application to Assessment of HIV Treatment Failure.

Journal of the American Statistical Association

Tao Liu, Joseph W Hogan, Lisa Wang, Shangxuan Zhang, Rami Kantor

Affiliations

  1. Assistant Professor, Department of Biostatistics, Center for Statistical Sciences, Brown University School of Public Health, Providence, RI 02912.
  2. Professor, Department of Biostatistics, Center for Statistical Sciences, Brown University School of Public Health, Providence, RI 02912.
  3. Graduate Student, Department of Biostatistics, Center for Statistical Sciences, Brown University School of Public Health, Providence, RI 02912.
  4. Statistical Programmer, Memorial Sloan-Kettering Cancer Center, New York City, NY 10016.
  5. Associate Professor of Medicine, Division of Infectious Diseases, the Alpert Medical School of Brown University, Providence, RI 02912.

PMID: 24672142 PMCID: PMC3963362 DOI: 10.1080/01621459.2013.810149

Abstract

The World Health Organization (WHO) guidelines for monitoring the effectiveness of HIV treatment in resource-limited settings (RLS) are mostly based on clinical and immunological markers (e.g., CD4 cell counts). Recent research indicates that the guidelines are inadequate and can result in high error rates. Viral load (VL) is considered the "gold standard", yet its widespread use is limited by cost and infrastructure. In this paper, we propose a diagnostic algorithm that uses information from routinely-collected clinical and immunological markers to guide a selective use of VL testing for diagnosing HIV treatment failure, under the assumption that VL testing is available only at a certain portion of patient visits. Our algorithm identifies the patient sub-population, such that the use of limited VL testing on them minimizes a pre-defined risk (e.g., misdiagnosis error rate). Diagnostic properties of our proposal algorithm are assessed by simulations. For illustration, data from the Miriam Hospital Immunology Clinic (RI, USA) are analyzed.

Keywords: Antiretroviral failure; HIV/AIDS; ROC; constrained optimization; resource limited; tripartite classification

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