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

ABSTRACT: Randomization inference is a powerful tool in early phase vaccine trials when estimating the causal effect of a regimen against a placebo or another regimen. Randomization-based inference often focuses on testing either Fisher's sharp null hypothesis of no treatment effect for any participant 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 to a special case where naïve participants are expec

SUBMITTER: Chen Z 

PROVIDER: S-EPMC11466280 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature