Randomization inference for stepped-wedge designs with noncompliance with application to a palliative care pragmatic trial
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911161135398912 |
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| author | Zhang, Jeffrey Chen, Zhe Courtright, Katherine R. Halpern, Scott D. Harhay, Michael O. Small, Dylan S. Li, Fan |
| author_facet | Zhang, Jeffrey Chen, Zhe Courtright, Katherine R. Halpern, Scott D. Harhay, Michael O. Small, Dylan S. Li, Fan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_14598 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Randomization inference for stepped-wedge designs with noncompliance with application to a palliative care pragmatic trial Zhang, Jeffrey Chen, Zhe Courtright, Katherine R. Halpern, Scott D. Harhay, Michael O. Small, Dylan S. Li, Fan Methodology Applications 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. |
| title | Randomization inference for stepped-wedge designs with noncompliance with application to a palliative care pragmatic trial |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2509.14598 |