Clinical trials : journal of the Society for Clinical Trials
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Comparative effectiveness research (CER) is still an evolving framework for which much needs to be done to improve the ability of randomized controlled trials (RCTs) to supply the necessary evidence. Perhaps, most important is to start with a clearly specified decision and decision maker in mind when the RCTs are designed. Second is to initiate RCTs with clinically relevant outcomes and comparators earlier in the evaluation process. ⋯ It will be necessary to borrow observational methodologies and approaches to extract meaningful causal and subgroup inferences from such trials. Process variables should be seen as potentially part of that framework of effect-modifying factors, perhaps amenable to embedded experimental assessment with a trial. Perhaps most importantly, we need to improve the nationwide CER infrastructure to allow for rapid initiation and accrual for CER trials to reduce the trade-off that often exists between the speed of evidence development and its quality.
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Randomized Controlled Trial
Factors influencing enrollment of African Americans in the Look AHEAD trial.
Many factors have been identified that influence the recruitment of African Americans into clinical trials; however, the influence of eligibility criteria may not be widely appreciated. We used the experience from the Look AHEAD (Action for Health in Diabetes) trial screening process to examine the differential impact eligibility criteria had on the enrollment of African Americans compared to other volunteers. ⋯ Compared to non-African Americans, African American were more often ineligible for the Look AHEAD trial due to comorbid conditions. Monitoring trial eligibility criteria for differential impact, and modifying them when appropriate, may ensure greater enrollment yields.
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The intention-to-treat comparison is the primary, if not the only, analytic approach of many randomized clinical trials. ⋯ We recommend that all randomized clinical trials with substantial lack of adherence or loss to follow-up are analyzed using different methods. These include an intention-to-treat analysis to estimate the effect of assigned treatment and 'as treated' and 'per protocol' analyses to estimate the effect of treatment after appropriate adjustment via inverse probability weighting or g-estimation.