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.

翻译：随机化推断是早期疫苗试验中评估一种方案相对于安慰剂或其他方案因果效应的强大工具。传统上，基于随机化的推断通常聚焦于检验费希尔关于任何单位均无治疗效应的严格零假设，或内曼关于样本平均治疗效应为零的弱零假设。近期许多研究探索了对治疗效应分布的其他汇总统计量进行精确随机化推断，例如治疗效应分布函数的分位数。本文系统综述了针对个体治疗效应分位数进行精确随机化推断的方法，并通过整合疫苗试验中常可获得的辅助信息对部分结果进行了推广。这些方法适用于四种场景：（i）一种方案下潜在结果恒定的随机对照试验；（ii）对潜在结果无任何限制的随机对照试验；（iii）对潜在结果设有用户指定边界的随机对照试验；以及（iv）比较两个非随机化、可能存在混杂因素的治疗组的匹配研究。随后我们进行了两项广泛的模拟研究：一项比较各方法在多种实际临床情境下的性能，另一项评估这些方法在排序和推进实验性疗法中的实用性。我们将这些方法应用于一项早期临床试验——HIV疫苗试验网络研究086（HVTN 086），以展示其应用价值。