The role of randomization inference in unraveling individual treatment effects in clinical trials: Application to HIV vaccine trials

Randomization inference is a powerful tool in early phase vaccine trials to estimate the causal effect of a regimen against a placebo or another regimen. Traditionally, randomization-based inference often focuses on testing either Fisher's sharp null hypothesis of no treatment effect for any unit or Neyman's weak null hypothesis of no sample average treatment effect. Many recent efforts have explored conducting exact randomization-based inference for other summaries of the treatment effect profile, for instance, quantiles of the treatment effect distribution function. In this article, we systematically review methods that conduct exact, randomization-based inference for quantiles of individual treatment effects (ITEs) and extend some results by incorporating auxiliary information often available in a vaccine trial. These methods are suitable for four scenarios: (i) a randomized controlled trial (RCT) where the potential outcomes under one regimen are constant; (ii) an RCT with no restriction on any potential outcomes; (iii) an RCT with some user-specified bounds on potential outcomes; and (iv) a matched study comparing two non-randomized, possibly confounded treatment arms. We then conduct two extensive simulation studies, one comparing the performance of each method in many practical clinical settings and the other evaluating the usefulness of the methods in ranking and advancing experimental therapies. We apply these methods to an early-phase clinical trail, HIV Vaccine Trials Network Study 086 (HVTN 086), to showcase the usefulness of the methods.