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Randomization inference is a powerful tool in early phase vaccine trials whenestimating the causal effect of a regimen against a placebo or another regimen.Randomization-based inference often focuses on testing either Fisher's sharpnull hypothesis of no treatment effect for any participant or Neyman's weaknull hypothesis of no sample average treatment effect. Many recent efforts haveexplored conducting exact randomization-based inference for other summaries ofthe treatment effect profile, for instance, quantiles of the treatment effectdistribution function. In this article, we systematically review methods thatconduct exact, randomization-based inference for quantiles of individualtreatment effects (ITEs) and extend some results to a special case wherena\"ive participants are expected not to exhibit responses to highly specificendpoints. These methods are suitable for completely randomized trials,stratified completely randomized trials, and a matched study comparing twonon-randomized arms from possibly different trials. We evaluate the usefulnessof these methods using synthetic data in simulation studies. Finally, we applythese methods to HIV Vaccine Trials Network Study 086 (HVTN 086) and HVTN 205and showcase a wide range of application scenarios of the methods. R code thatreplicates all analyses in this article can be found in first author's GitHubpage at https://github.com/Zhe-Chen-1999/ITE-Inference.

The role of randomization inference in unraveling individual treatment effects in early phase vaccine trials