Work

Causal sensitivity analyses for indirect and spillover mediation effects in cluster-randomized trials: Application to a pragmatic trial on chronic pain management

Year: 2026

Type: preprint

Abstract: Causal mediation in cluster-randomized trials can clarify whether a cluster-level intervention changes patient outcomes through patient- and peer-level processes, but identification relies on untestable cross-world assumptions. We develop a two-part sensitivity analysis for violations of two distinct cross-world independence assumptions. The first component adapts bridge score sensitivity analysis to assess residual mediator-outcome confounding with clustered mediator exposures; the second uses a Gaussian copula to assess cross-world dependence between an individual's treated mediator and summaries of control-arm peer mediators. The resulting framework parameterizes unidentifiable dependence structures using interpretable correlation parameters and combines them with a flexible Bayesian nonparametric model to characterize the plausible range of the target estimands. A nested common-atoms enriched Dirichlet process models the observed data and supports posterior-predictive standardization. We apply the framework to the Pain Program for Active Coping and Training trial, a primary care-embedded cognitive behavioral therapy intervention for adults with chronic pain receiving long-term opioid therapy. The intervention had a negative total effect on twelve-month pain-related disability. The natural indirect and direct effect decomposition was sensitive to plausible directional residual confounding, whereas the spillover and individual mediation effect decomposition changed little across the copula sensitivity analyses and attributed most of the negative indirect signal to the individual's mediator. Jointly relaxing both assumptions substantially increased uncertainty about the pathways. The analysis supports an overall improvement in disability and a plausible individual pain-impact pathway, while showing that detailed mechanistic claims remain assumption-dependent.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.35884

Authors: Ohnishi, Yuki, Daniels, Michael J., Li, Fan

Institutions: University of Florida, Yale University

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Patient-Centered Outcomes Research Institute, National Institutes of Health