2025/09/18 by Jeffrey Zhang, Zhe Chen, Zhang, Jeffrey +11
Mathematics · Medicine · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME) #Palliative Care and End-of-Life Issues #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2509.14598
openalex publication_date 2025/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
While palliative care is increasingly commonly delivered to hospitalized patients with serious illnesses, few studies have estimated its causal effects. Courtright et al. (2016) adopted a cluster-randomized stepped-wedge design to assess the effect of palliative care on a patient-centered outcome. The randomized intervention was a nudge to administer palliative care but did not guarantee receipt of palliative care, resulting in noncompliance (compliance rate ~30%). A subsequent analysis using methods suited for standard trial designs produced statistically anomalous results, as an intention-to-treat analysis found no effect while an instrumental variable analysis did (Courtright et al., 2024). This highlights the need for a more principled approach to address noncompliance in stepped-wedge designs. We provide a formal causal inference framework for the stepped-wedge design with noncompliance by introducing a relevant causal estimand and corresponding estimators and inferential procedures. Through simulation, we compare an array of estimators across a range of stepped-wedge designs and provide practical guidance in choosing an analysis method. Finally, we apply our recommended methods to reanalyze the trial of Courtright et al. (2016), producing point estimates suggesting a larger effect than the original analysis of (Courtright et al., 2024), but intervals that did not reach statistical significance.