On Modeling HIV and T Cells In Vivo: Assessing Causal Estimators in Vaccine Trials

The first efficacy trials—named STEP—of a T cell vaccine against HIV/AIDS began in 2004. The unprecedented structure of these trials raised new modeling and statistical challenges. Is it plausible that memory T cells, as opposed to antibodies, can actually prevent infection? If they fail at prevention, to what extent can they ameliorate disease? And how do we estimate efficacy in a vaccine trial with two primary endpoints, one traditional, one entirely novel (viral load after infection), and where the latter may be influenced by selection bias due to the former? In preparation for the STEP trials, biostatisticians developed novel techniques for estimating a causal effect of a vaccine on viral load, while accounting for post-randomization selection bias. But these techniques have not been tested in biologically plausible scenarios. We introduce new stochastic models of T cell and HIV kinetics, making use of new estimates of the rate that cytotoxic T lymphocytes—CTLs; the so-called killer T cells—can kill HIV-infected cells. Based on these models, we make the surprising discovery that it is not entirely implausible that HIV-specific CTLs might prevent infection—as the designers explicitly acknowledged when they chose the endpoints of the STEP trials. By simulating thousands of trials, we demonstrate that the new statistical methods can correctly identify an efficacious vaccine, while protecting against a false conclusion that the vaccine exacerbates disease. In addition to uncovering a surprising immunological scenario, our results illustrate the utility of mechanistic modeling in biostatistics.Synopsis: In traditional biostatistics, mechanistic modeling of the relevant biology usually plays no role, because regulatory agencies will not, quite understandably, license vaccines or drugs on the basis of theories. But the second wave of trials of HIV vaccines will test two conjectures simultaneously. The theoretical possibility that these new, nonclassical, T cell–directed vaccines will prevent some infections while only ameliorating disease in others required biostatisticians to invent new ways of estimating vaccine efficacy. When only the one traditional endpoint—infection—is analyzed, the randomization to vaccine or placebo groups protects against bias. But the new techniques required input from experts on the plausible range of bias introduced by post-randomization selection (by infected state) for the second analysis. Here mechanistic modeling can play a role in evaluating the statistical methodology in biologically plausible settings. By simulating thousands of trials using their models, Wick, Gilbert, and Self were able to demonstrate that the methods protected from falsely concluding a harmful effect of the vaccine on disease. They also noted that the so-called killer T cells, as opposed to antibodies raised by a traditional vaccine, may actually be able to prevent some infections—a conclusion rather surprising for most immunologists and virologists, but which had to be allowed for when designing the vaccine trials.