Causal Ensembles for Evaluating the Effect of Delayed Switch to Second-Line Antiretroviral Regimens
Transitioning from a failing antiretroviral regimen to a new regimen is a critical period in managing treatments to suppress HIV-1 RNA because it can have lasting effects on the durability of disease and likelihood of developing resistant mutations. Evalu
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Causal Ensembles for Evaluating the Effect of Delayed Switch to Second-Line Antiretroviral Regimens Li Li and Brent A. Johnson
Abstract Transitioning from a failing antiretroviral regimen to a new regimen is a critical period in managing treatments to suppress HIV-1 RNA because it can have lasting effects on the durability of disease and likelihood of developing resistant mutations. Evaluating the timing of a switch to the subsequent therapy is difficult because patients are not randomly assigned to switch failing regimens at designed time points. Li et al. (J. Am. Stat. Assoc. 107:542–554, 2012) proposed and applied doubly robust semi-parametric methods to evaluate the effect of early versus late regimen switch in a two-stage design setting. These semi-parametric estimators are consistent if a parametric treatment model is correctly specified and achieve optimal performance if a parametric outcome model is also correctly specified. Here, we propose a new non-parametric estimator of the same causal estimand using an ensemble-type statistical learner. Compared to earlier estimators, the proposed estimator requires fewer model assumptions and can easily accommodate a large number of potential confounders. We illustrate the methods through simulation studies and application to data from the AIDS Clinical Trials Group Study A5095.
1 Introduction In current clinical practice, HIV-1 infected patients are treated through a sequence of combined antiretroviral therapies (cART). Although HIV-1 is a viral agent and causes acquired immunodeficiency syndrome (AIDS), modern treatment successes and the lack of a cure suggest similarities in treatment of HIV-1 infection and a chronic disease. The primary goal of cART is to reduce viremia below a limit of
L. Li Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA e-mail: [email protected] B.A. Johnson () Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, NY 14642, USA e-mail: [email protected] © Springer International Publishing Switzerland 2016 H. He et al. (eds.), Statistical Causal Inferences and Their Applications in Public Health Research, ICSA Book Series in Statistics, DOI 10.1007/978-3-319-41259-7_11
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detection, but providers also make treatment decisions to help patients manage adverse side effects and opportunistic infections. For a variety of reasons, including co-morbidities, genetic mutations, and poor adherence, patients eventually fail their current cART and move to the next-in-line cART. Then, similar to individuals that live with chronic diseases, HIV-1 infected individuals transition from treatment regimen to regimen, as necessary, until all treatment options have been exhausted or death. Despite all that the scientific community has learned about treating HIV-1 infection and AIDS over the last four decades, there is still much that is unknown. In particular, there are many open questions about the timing of treatment de
