Presenting a randomized controlled trial

Author(s):  
Janet L. Peacock ◽  
Sally M. Kerry ◽  
Raymond R. Balise

Chapter 12 covers all aspects of reporting a randomized controlled trial, and illustrates the use of the CONSORT statement and its checklist to report a trial. It discusses what is meant by intention-to-treat analysis. It considers the presentation of trials with other designs, particularly cluster randomized trials.

Author(s):  
Janet L. Peacock ◽  
Sally M. Kerry

Chapter 12 covers presenting a randomised controlled trial, including the CONSORT statement and checklist, intention to treat analysis, and presenting trials with other designs.


2008 ◽  
Vol 27 (27) ◽  
pp. 5565-5577 ◽  
Author(s):  
Booil Jo ◽  
Tihomir Asparouhov ◽  
Bengt O. Muthén

2018 ◽  
Vol 47 (8) ◽  
pp. 885-889 ◽  
Author(s):  
Anette Andersen ◽  
Lotus S. Bast ◽  
Pernille Due ◽  
Lau C. Thygesen

Aims:Review studies on the long-term effects of school-based smoking interventions show mixed results. X:IT was a three-year cluster randomized controlled trial to prevent uptake of smoking among Danish students from age 13 years until age 15 years which previously proved effective in preventing smoking after the first year of intervention. The aim of this paper was to conduct the pre-planned analyses of the effects of the X:IT intervention on smoking after the second year. Methods: We used self-reported questionnaire data from students at baseline, first, second, and third follow-up ( n at second follow-up=3269, response rate=79.4%). Data from third follow-up were not suitable for analysis. Outcome measure: ‘current smoking’, dichotomised into smoke daily, weekly, monthly or more seldom versus do not smoke. We performed multilevel, logistic regression analyses of available cases and intention-to-treat (ITT) analyses, replacing missing outcome values by multiple imputation. Results: The prevalence of smoking increased from 5.8% at baseline to 17.0% at second follow-up among students at intervention schools, and from 7.6% to 18.7% among students at control schools. Analyses of available cases and ITT analyses did not support X:IT being effective in preventing smoking after the second year of intervention. Conclusions: Although X:IT was effective after the first year of intervention, we were not able to demonstrate any effects after the second year. Implementation of the intervention was lower in the second year compared to the first year which indicates that the missing effect of the intervention at second follow-up is due to lack of implementation.


Blood ◽  
2018 ◽  
Vol 132 (2) ◽  
pp. 223-231 ◽  
Author(s):  
Pieter F. van der Meer ◽  
Paula F. Ypma ◽  
Nan van Geloven ◽  
Joost A. van Hilten ◽  
Rinie J. van Wordragen-Vlaswinkel ◽  
...  

Key Points Pathogen-inactivated platelets were noninferior in preventing bleeding only in intention-to-treat analysis. In contrast to animal models, alloimmunization could not be prevented when using pathogen-inactivated platelets.


2020 ◽  
Vol 17 (6) ◽  
pp. 627-636
Author(s):  
Stacia M DeSantis ◽  
Ruosha Li ◽  
Yefei Zhang ◽  
Xueying Wang ◽  
Sally W Vernon ◽  
...  

Background Cluster randomized trials are designed to evaluate interventions at the cluster or group level. When clusters are randomized but some clusters report no or non-analyzable data, intent-to-treat analysis, the gold standard for the analysis of randomized controlled trials, can be compromised. This article presents a very flexible statistical methodology for cluster randomized trials whose outcome is a cluster-level proportion (e.g. proportion from a cluster reporting an event) in the setting where clusters report non-analyzable data (which in general could be due to nonadherence, dropout, missingness, etc.). The approach is motivated by a previously published stratified randomized controlled trial called, “The Randomized Recruitment Intervention Trial (RECRUIT),” designed to examine the effectiveness of a trust-based continuous quality improvement intervention on increasing minority recruitment into clinical trials (ClinicalTrials.gov Identifier: NCT01911208). Methods The novel approach exploits the use of generalized estimating equations for cluster-level reports, such that all clusters randomized at baseline are able to be analyzed, and intervention effects are presented as risk ratios. Simulation studies under different outcome missingness scenarios and a variety of intra-cluster correlations are conducted. A comparative analysis of the method with imputation and per protocol approaches for RECRUIT is presented. Results Simulation results show the novel approach produces unbiased and efficient estimates of the intervention effect that maintain the nominal type I error rate. Application to RECRUIT shows similar effect sizes when compared to the imputation and per protocol approach. Conclusion The article demonstrates that an innovative bivariate generalized estimating equations framework allows one to implement an intent-to-treat analysis to obtain risk ratios or odds ratios, for a variety of cluster randomized designs.


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