ArticleContemporary clinical trials2024
Statistical analysis plan for the NU IMPACT stepped-wedge cluster randomized trial.
Article in Contemporary clinical trials, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Implementation outcomes of a symptom management intervention in ambulatory oncology practices evaluated using a cluster randomized stepped-wedge trial design.Implementation science : IS · 2025Trial
- What scientific inferences can be made with randomized implementation rollout trials.Implementation science : IS · 2025Article
- Rollout trial designs in implementation research are often necessary and sometimes preferred.Implementation science : IS · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundAs part of the IMPACT Consortium of three effectiveness-implementation trials, the NU IMPACT trial was designed to evaluate implementation and effectiveness outcomes for an electronic health record (EHR)-embedded symptom monitoring and management program for outpatient cancer care. NU IMPACT uses a unique stepped-wedge cluster randomized design, involving six clusters of 26 clinics, for evaluation of implementation outcomes with an embedded patient-level randomized trial to evaluate effectiveness outcomes. Collaborative, consortium-wide efforts to ensure use of the most robust and recent analytic methodologies for stepped-wedge trials motivated updates to the statistical analysis plan for implementation outcomes in the NU IMPACT trial.
methodsIn the updated statistical analysis plan for NU IMPACT, the primary implementation outcome patient adoption, as measured by clinic-level monthly proportions of patient engagement with the EHR-based cancer symptom monitoring system, will be analyzed using generalized least squares linear regression with auto-regressive errors and adjustment for cluster and time effects (underlying secular trends). A similar strategy will be used for secondary patient and provider implementation outcomes. DISCUSSION: The analytic updates described here resulted from highly iterative, collaborative efforts among statisticians, implementation scientists, and trial leads in the IMPACT Consortium. This updated statistical analysis plan will serve as the a priori specified approach for analyzing implementation outcomes for the NU IMPACT trial.
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