Clinical trials : journal of the Society for Clinical Trials
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Our purpose was to identify physicians' individual characteristics, attitudes, and organizational contextual factors associated with higher enrollment of patients in cancer clinical trials among physician participants in the National Cancer Institute's Community Clinical Oncology Program (CCOP). We hypothesized that physicians' individual characteristics, such as age, medical specialty, tenure, CCOP organizational factors (i.e. policies and procedures to encourage enrollment), and attitudes toward participating in CCOP would directly determine enrollment. We also hypothesized that physicians' characteristics and CCOP organizational factors would influence physicians' attitudes toward participating in CCOP, which in turn would predict enrollment. ⋯ We examined whether individual physicians' characteristics and attitudes, as well as CCOP organizational factors, influenced patient enrollment in cancer clinical trials among CCOP physicians. Physician attitudes and CCOP organizational factors had positive direct effects, but not indirect effects, on physician enrollment of patients. Our results could be used to develop physician-directed strategies aimed at increasing involvement in clinical research. For example, administrators may want to ensure physicians have access to support staff to help screen and enroll patients or institute minimum accrual expectations. Our results also highlight the importance of recruiting physicians for volunteer clinical research programs whose attitudes and values align with programmatic goals. Given that physician involvement is a key determinant of patient enrollment in clinical trials, these interventions could expand the overall number of patients involved in cancer research. These strategies will be increasingly important as the CCOP network continues to evolve.
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Central to the design of a randomised controlled trial (RCT) is a calculation of the number of participants needed. This is typically achieved by specifying a target difference, which enables the trial to identify a difference of a particular magnitude should one exist. Seven methods have been proposed for formally determining what the target difference should be. However, in practice, it may be driven by convenience or some other informal basis. It is unclear how aware the trialist community is of these formal methods or whether they are used. ⋯ Substantial variations in practice exist with awareness, use, and willingness to recommend methods varying substantially. The findings support the view that sample size calculation is a more complex process than would appear to be the case from trial reports and protocols. Guidance on approaches for sample size estimation may increase both awareness and use of appropriate formal methods.
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Randomized Controlled Trial
A community consultation survey to evaluate support for and success of the IMMEDIATE trial.
The IMMEDIATE (Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care) Trial, a randomized controlled double-blind clinical effectiveness trial of glucose-insulin-potassium (GIK) administered in ambulances in the out-of-hospital setting, used the Exception from Informed Consent Requirements (EFIC) for Emergency Research under Title 21 of the Code of Federal Regulations. EFIC requirements include community consultation that typically involves using a variety of communication methods and venues to inform the public of the research and to receive their feedback. Although not the primary purpose of the community consultation process, a common concern to research sponsors, staff, and institutional review boards (IRBs) is whether there will be a sufficient number of participants to justify mounting a study in their community. Information from community consultation regarding the community acceptance might inform this question. ⋯ The survey-based approach to community consultation proved to be an efficient way to obtain representative input from potential clinical trial participants. The survey data generated a relatively good and conservative estimate of the ultimate rate of trial enrollment. This information could be useful to investigators and IRBs in projecting enrollment for clinical trials using EFIC.