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Tytuł:
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Comparison of analysis methods and design choices for treatment-by-period interaction in unidirectional switch designs: a simulation study.
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Autorzy:
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Zhan Z; Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands. .
de Bock GH; Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
van den Heuvel ER; Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.
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Źródło:
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BMC medical research methodology [BMC Med Res Methodol] 2022 Nov 17; Vol. 22 (1), pp. 294. Date of Electronic Publication: 2022 Nov 17.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: London : BioMed Central, [2001-
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MeSH Terms:
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Cluster Analysis*
Humans ; Computer Simulation ; Linear Models
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References:
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Contributed Indexing:
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Keywords: Clustered randomized controlled trial; Hybrid design; Monte Carlo simulation; Power analysis; Stepped wedge design
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Entry Date(s):
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Date Created: 20221118 Date Completed: 20221121 Latest Revision: 20221204
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Update Code:
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20240104
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PubMed Central ID:
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PMC9673415
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DOI:
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10.1186/s12874-022-01765-9
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PMID:
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36396984
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Background: Due to identifiability problems, statistical inference about treatment-by-period interactions has not been discussed for stepped wedge designs in the literature thus far. Unidirectional switch designs (USDs) generalize the stepped wedge designs and allow for estimation and testing of treatment-by-period interaction in its many flexible design forms.
Methods: Under different forms of the USDs, we simulated binary data at both aggregated and individual levels and studied the performances of the generalized linear mixed model (GLMM) and the marginal model with generalized estimation equations (GEE) for estimating and testing treatment-by-period interactions.
Results: The parallel group design had the highest power for detecting the treatment-by-period interactions. While there was no substantial difference between aggregated-level and individual-level analysis, the GLMM had better point estimates than the marginal model with GEE. Furthermore, the optimal USD for estimating the average treatment effect was not efficient for treatment-by-period interaction and the marginal model with GEE required a substantial number of clusters to yield unbiased estimates of the interaction parameters when the correlation structure is autoregressive of order 1 (AR1). On the other hand, marginal model with GEE had better coverages than GLMM under the AR1 correlation structure.
Conclusion: From the designs and methods evaluated, in general, parallel group design with a GLMM is, preferred for estimation and testing of treatment-by-period interaction in a clustered randomized controlled trial for a binary outcome.
(© 2022. The Author(s).)
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