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Randomization inference is a powerful tool in early phase vaccine trials toestimate the causal effect of a regimen against a placebo or another regimen.Traditionally, randomization-based inference often focuses on testing eitherFisher's sharp null hypothesis of no treatment effect for any unit or Neyman'sweak null hypothesis of no sample average treatment effect. Many recent effortshave explored conducting exact randomization-based inference for othersummaries of the treatment effect profile, for instance, quantiles of thetreatment effect distribution function. In this article, we systematicallyreview methods that conduct exact, randomization-based inference for quantilesof individual treatment effects (ITEs) and extend some results by incorporatingauxiliary information often available in a vaccine trial. These methods aresuitable for four scenarios: (i) a randomized controlled trial (RCT) where thepotential outcomes under one regimen are constant; (ii) an RCT with norestriction on any potential outcomes; (iii) an RCT with some user-specifiedbounds on potential outcomes; and (iv) a matched study comparing twonon-randomized, possibly confounded treatment arms. We then conduct twoextensive simulation studies, one comparing the performance of each method inmany practical clinical settings and the other evaluating the usefulness of themethods in ranking and advancing experimental therapies. We apply these methodsto an early-phase clinical trail, HIV Vaccine Trials Network Study 086 (HVTN086), to showcase the usefulness of the methods.

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