Management of Antiretroviral Therapy for HIV Infection: Analyzing When to Change Therapy

Rebecca M. D'Amato (), Richard T. D'Aquila () and Lawrence M. Wein ()
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Rebecca M. D'Amato: Rand Corporation, Santa Monica, California 90407
Richard T. D'Aquila: Infectious Disease Unit and AIDS Research Center, Massachusetts General Hospital, Charlestown, Massachusetts 02129
Lawrence M. Wein: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Management Science, 2000, vol. 46, issue 9, 1200-1213

Abstract: We analyze two joint decisions in the management of HIV-infected patients on antiretroviral therapy: how frequently to measure a patient's virus level, and when to switch therapy. The underlying stochastic model captures the initial suppression and eventual rebound of the virus level in the blood of a typical HIV-infected patient undergoing treatment. We consider two classes of policies: a viral load policy, which triggers a change in therapy when the current virus level divided by the smallest level achieved thus far exceeds a prespecified threshold, and a proactive policy, which is similar to the viral load policy but also switches drugs at a prespecified time if no evidence of viral rebound has been seen. We find approximate analytical expressions for the probability of switching before the virus reaches its nadir (minimum value) and the mean delay in detection of viral rebound (i.e., the time interval from when the viral nadir occurs until the switch in therapy). Numerical results show that the proactive policy outperforms (i.e., a smaller detection delay for a given probability of prenadir switching) the viral load policy and recent recommendations by an expert AIDS panel, and may delay the onset of multidrug resistance in a significant proportion of patients who experience drug failure.

Keywords: AIDS; stochastic modeling; censored measurements; Slepian's inequality (search for similar items in EconPapers)
Date: 2000
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Citations: View citations in EconPapers (3)

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