Clinical trials : journal of the Society for Clinical Trials
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Intention-to-treat (ITT) analysis requires all randomised individuals to be included in the analysis in the groups to which they were randomised. However, there is confusion about how ITT analysis should be performed in the presence of missing outcome data. ⋯ Clinical trials should employ an ITT analysis strategy, comprising a design that attempts to follow up all randomised individuals, a main analysis that is valid under a stated plausible assumption about the missing data, and sensitivity analyses that include all randomised individuals in order to explore the impact of departures from the assumption underlying the main analysis. Following this strategy recognises the extra uncertainty arising from missing outcomes and increases the incentive for researchers to minimise the extent of missing data.
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Low compliance to randomized nondrug interventions can affect treatment estimates of clinical trials. Cluster-randomized crossover may be appropriate for increasing compliance in the out-of-hospital cardiac arrest setting. ⋯ We found a significant decrease in compliance to the AL versus AE cardiac arrest intervention as the elapsed time from start of treatment period increased. We did not find a difference in compliance immediately before and after a crossover. While these results suggest that future cluster with crossover trials in the out-of-hospital setting be designed with short treatment periods and frequent crossovers, provider logistical concerns must also be considered.
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Comparative Study
Taking the long view: how to design a series of Phase III trials to maximize cumulative therapeutic benefit.
Traditional clinical trial designs strive to definitively establish the superiority of an experimental treatment, which results in risk-adverse criteria and large sample sizes. Increasingly, common cancers are recognized as consisting of small subsets with specific aberrations for targeted therapy, making large trials infeasible. ⋯ It is worthwhile to consider a design paradigm that seeks to maximize the expected survival benefit across a series of trials, over a longer research horizon. In today's environment of constrained, biomarker-selected populations, our results indicate that smaller sample sizes and larger α values lead to greater long-term survival gains compared to traditional large trials designed to meet stringent criteria with a low efficacy bar.
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In neuroscience clinical research studies, much time and effort are devoted to deciding what data to collect and developing data collection forms and data management systems to capture the data. Many investigators receiving funding from National Institute of Neurological Disorders and Stroke (NINDS), the National Institutes of Health (NIH), are required to share their data once their studies are complete, but the multitude of data definitions and formats make it extremely difficult to aggregate data or perform meta-analyses across studies. ⋯ Version 1.0 of a set of CDEs has been published, but publication is not the end of the development process. All CDEs will be evaluated and revised at least annually to ensure that they reflect current clinical research practices in neuroscience.
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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.