Journal of clinical epidemiology
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Pragmatic trials may deliver real-world evidence on the added value of new medications compared with usual care and inform decision making earlier in development. This fifth paper in a series on pragmatic trials in the Journal discusses usual care as a comparator and the allocation of treatment strategies. The allocation and implementation of treatment strategies should resemble clinical practice as closely as possible. ⋯ Using clinical guidelines to define usual care can be helpful in standardizing comparator treatments; however, this may decrease the applicability of the results to real-life settings. Conversely, using multiple usual-care treatment arms will increase the complexity of the study. The specific objectives of the trial and design choices should be discussed with all stakeholders to realize the full potential of the pragmatic trial.
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Complex interventions are widely used in health care, public health, education, criminology, social work, business, and welfare. They have increasingly become the subject of systematic reviews and are challenging to effectively report. The Complex Interventions Methods Workgroup developed an extension to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Complex Interventions (PRISMA-CI). ⋯ The EE document explains the meaning and rationale for each unique PRISMA-CI checklist item and provides examples to assist systematic review authors in operationalizing PRISMA-CI guidance. The Complex Interventions Workgroup developed PRISMA-CI as an important start toward increased consistency in reporting of systematic reviews of complex interventions. Because the field is rapidly expanding, the Complex Interventions Methods Workgroup plans to re-evaluate periodically for the need to add increasing specificity and examples as the field matures.
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Results from pragmatic trials should reflect the comparative treatment effects encountered in patients in real-life clinical practice to guide treatment decisions. Therefore, pragmatic trials should focus on outcomes that are relevant to patients, clinical practice, and treatment choices. This sixth article in the series (see Box) discusses different types of outcomes and their suitability for pragmatic trials, design choices for measuring these outcomes, and their implications and challenges. ⋯ Methods that decrease bias or enhance precision of the results, such as standardization and blinding of outcome assessment, should be considered when a high risk of bias or high variability is expected. The selection of outcomes in pragmatic trials should be relevant for decision making and feasible in terms of executing the trial in the context of interest. Therefore, this should be discussed with all stakeholders as early as feasible to ensure the relevance of study results for decision making in clinical practice and the ability to perform the study.
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Comparative Study
Understanding the applicability of results from primary care trials: lessons learned from applying PRECIS-2.
To compare two approaches for trial teams to apply PRECIS-2 to pragmatic trials: independent scoring and scoring following a group discussion. ⋯ PRECIS-2 can facilitate information exchange within trial teams. To apply PRECIS-2 successfully, we recommend a discussion between those with detailed understanding of what usual care is for the intervention, the trial's design including operational and technical aspects, and the PRECIS-2 domains. For some cluster-randomized trials, greater insight may be gained by plotting two PRECIS-2 wheels, one at the individual participant level and another at the cluster level.
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Pragmatic trials aim to inform clinical decision making by measuring the effect of a treatment in clinical practice. The purpose of the PRECIS-2 tool is to support in designing a truly pragmatic trial. ⋯ The tool will prove particularly useful when implemented in the process of trial design. However, it is yet unclear how e.g., possible dependencies between PRECIS domains, or conducting a pragmatic trial within an existing data registry (e.g., electronic health records) affect the applicability of the tool.