The linear mixed model Written as an equation, the 'null' (no covariate) linear mixed model for a 2-level hierarchical study is:
BackgroundStepped wedge design (SWD) is a cluster randomized controlled trial (RCT) design that sequentially rolls out intervention to all clusters at varying time points. Being a relatively new design method, reporting quality has yet to be explored, and this review will seek to fill this gap in knowledge.ObjectivesThe objectives of this review are: 1) to assess the quality of SWD trial reports based on the CONSORT guidelines or CONSORT extension to cluster RCTs; 2) to assess the completeness of reporting of SWD trial abstracts using the CONSORT extension for abstracts; 3) to assess the reporting of sample size details in SWD trial reports or protocols; 4) to assess the completeness of reporting of SWD trial protocols according to SPIRIT guidelines; 5) to assess the consistency between the trial registration information and final SWD trial reports; and 6) to assess the consistency of what is reported in the abstracts and main text of the SWD trial reports. We will also explore factors that are associated with the completeness of reporting.MethodsWe will search MEDLINE, EMBASE, Web of Science, CINAHL, and PsycINFO for all randomized controlled trials utilizing SWD. Details from eligible papers will be extracted in duplicate. Demographic statistics obtained from the data extraction will be analyzed to answer the primary objectives pertaining to the reporting quality of several aspects of a published paper, as well as to explore possible temporal trends and consistency between abstracts, trial registration information, and final published articles.DiscussionFindings from this review will establish the reporting quality of SWD trials and inform academics and clinicians on their completeness and consistency. Results of this review will influence future trials and improve the overall quality and reporting of SWD trials.
BackgroundThe stepped wedge trial (SWT) design is a type of the randomized clinical trial (RCT) design in which clusters or individuals are randomly and sequentially crossed over from control to intervention over a number of time periods. Trials using SWT design have become increasingly popular in medical, behavioral and social sciences research. Therefore, complete and transparent reporting of these studies is crucial. In particular, the quality of the abstracts of their reports is important because these may be the only accessible sources for their results.ObjectiveThe aims of this survey were to evaluate the reporting quality of SWT abstracts and to identify factors contributing to better reporting quality.MethodsWe performed literature searches to identify relevant articles in English published from November 1987 to October 2016 in the following electronic databases: Medline, Embase, Web of Science, CINAHL, and PsycINFO. At least two reviewers examined the quality of abstract reporting using the 17-item CONSORT (CONsolidated Standards Of Reporting Trials) Extension for Abstracts tool. Poisson regression models for incidence rate ratio (IRR) were used to identify factors associated with reporting quality (e.g., CONSORT endorsement, the number of authors, abstract format).ResultsA total of 92 eligible articles were identified. Only 6 from the 17 items were reported in more than 80% of the articles (e.g., the statement of conclusions, contact details for the corresponding author). In the multivariable analysis, the year of publication since 2008 (IRR: 1.16; 95% confidence interval (CI): 1.02, 1.33), journal endorsement of the CONSORT Statement (IRR: 1.15; 95% CI: 1.01, 1.31), and multiple authorship (IRR 1.13, 95% CI: 1.01, 1.27) were significantly associated with better reporting quality.ConclusionThe quality of reporting of SWT abstracts was suboptimal, although there have been some significant improvements since 2008. Endorsement of the CONSORT Statement by journals is an essential element of improvement strategies. Also, multiple authorship is significantly associated with better quality of abstract reporting.
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