Causal inference in randomized trials with partial clustering.

ABSTRACT:

Background

Participant dependence, if present, must be accounted for in the analysis of randomized trials. This dependence, also referred to as "clustering," can occur in one or more trial arms. This dependence may predate randomization or arise after randomization. We examine three trial designs: one "fully clustered" (where all participants are dependent within clusters or groups) and two "partially clustered" (where some participants are dependent within clusters and some participants are completely independent of all others).

Methods

For these three designs, we (1) use causal models to non-parametrically describe the data generating process and formalize the dependence in the observed data distribution; (2) develop a novel implementation of targeted minimum loss-based es

SUBMITTER: Nugent JR 

PROVIDER: S-EPMC12355196 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature