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Using Mechanistic Models to Simulate Comparative Effectiveness Trials of Therapy and to Estimate Long-term Outcomes in HIV Care

Roberts, Mark S. MD, MPP*†‡; Nucifora, Kimberly A. MS§; Braithwaite, R. Scott MD, MS§

doi: 10.1097/MLR.0b013e3181e2b744
Comparative Effectiveness

Background: In HIV care, it is difficult to decide when to initiate therapy, which drugs to use for initial treatment, and which drugs to use if drug resistance develops. With hundreds of possible drug regimens available and variable patterns of drug resistance, randomized controlled trials cannot answer all HIV treatment decisions. Mechanistic models of HIV infection can be used to conduct virtual therapeutic trials with the goal of predicting outcomes, some of which are long-term and may not fall within the time frame of a typical therapeutic trial.

Methods: We used a previously developed and validated model of HIV infection to replicate 2 arms of an HIV initial treatment trial (ACTG A5142) and predict long-term outcomes. The model incorporated data about biologic processes involved in the development of drug resistance.

Results: The model reproduced the proportion that developed AIDS (0.04 and 0.05 for the efavirenz arm and lopinavir arms, respectively, vs. 0.04 and 0.06 for the trial), the development of virologic failure (0.27 and 0.33 for the Efavirenz arm and lopinavir arms, respectively, vs. 0.24 and 0.37 for the trial), and drug resistance. The hazard ratio for the time to treatment failure, a combination of resistance and other causes (0.96 for the model vs. 0.75 for the trial; 95% confidence interval, 0.57–0.98), and changes in CD4 cell count, were less accurate. The model estimated longer-term life expectancy, quality-adjusted life expectancy, and HIV-related deaths.

Conclusions: Mechanistic models of HIV infections have the potential to be useful in comparative effectiveness research.

From the *Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania; †Section of Decision Sciences and Clinical Systems Modeling, Division of General Internal Medicine, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania; ‡Department of Industrial Engineering, School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania; and §Section of Value and Comparative Effectiveness, Division of General Internal Medicine, Department of Medicine, School of Medicine, New York University, New York, NY.

This work was partially supported by U01 AA013566-06 and R01 AA017385-01 from the National Institutes of Health.

Reprints: Mark S. Roberts, MD, MPP, Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, 130 De Soto Street, Room A620, Pittsburgh, PA 15261. E-mail: mroberts@pitt.edu.

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© 2010 Lippincott Williams & Wilkins, Inc.