A Case Study in Personalized Medicine: Rilpivirine Versus Efavirenz for Treatment-Naive HIV Patients

Wei Liu, Zhiwei Zhang, Lei Nie and Guoxing Soon

Journal of the American Statistical Association, 2017, vol. 112, issue 520, 1381-1392

Abstract: Rilpivirine and efavirenz are two major nonnucleoside reverse transcriptase inhibitors currently available in the U.S. for treatment-naive adult patients infected with human immunodeficiency virus (HIV). Two randomized clinical trials comparing the two drugs suggested that their relative efficacy may depend on baseline viral load and CD4 cell count. This article is concerned with the potential utilities of these biomarkers in developing individualized treatment regimes that attempt to maximize the virologic response rate or the median of a composite outcome that combines virologic response with change in CD4 cell count (dCD4). Working with the median composite outcome removes the need to assign numerical values to the composite outcome, as would be necessary if we were to maximize its mean, and reduces the influence of extreme dCD4 values. To estimate the target quantities for a given treatment regime, we use G-computation, inverse probability weighting (IPW), and augmented IPW methods to deal with censoring and missing data under a monotone coarsening framework. The resulting estimates form the basis for optimization in a class of candidate regimes indexed by a small number of parameters. A cross-validation procedure is used to remove the resubstitution bias in evaluating an optimized treatment regime. Application of these methods to the HIV trial data yields candidate regimes of different forms together with cross-validated performance measure estimates, which suggest that optimized treatment regimes may be able to improve virologic response (but not the composite outcome) over uniform regimes that prescribe one drug for all patients. Supplementary materials for this article are available online.

Date: 2017
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DOI: 10.1080/01621459.2017.1280404

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