1. A review on generation of real-world evidence
- Author
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M. Shunmugavelu, Ashish Sehgal, Jayanta Ku. Panda, and Brij Makkar
- Subjects
Developmental stage ,Policy making ,Computer science ,business.industry ,Endocrinology, Diabetes and Metabolism ,media_common.quotation_subject ,Technical standard ,Real world evidence ,Health outcomes ,Risk analysis (engineering) ,Health care ,Internal Medicine ,Quality (business) ,Treatment decision making ,business ,media_common - Abstract
Real-world evidence can generate credible evidence to inform treatment decisions. Real-world evidence is in developmental stage and is fast evolving yet there are many unexplained attributes of real-world evidence. Real-world evidence informs benefit-risk decisions and is increasingly being used to support regulatory decision making. Potential benefits of real-world data include determination of extended outcomes including long-term outcomes, opportunities to partner with patients in innovative ways, and reduction in time and cost to generate dependable evidence. Limitations of real-world evidence include uncertainty in the quality of datasets and lack methodologic rigor in real-world studies. Use of real-world evidence for healthcare practices and policies is limited. Ensuring completeness, homogeneity, and linkage of datasets can enhance utility for epidemiological investigations and improvement in health outcomes. Research should be strengthened for real-world studies and technical standards should be reinforced. Collaborations of stakeholders is key to formulation and adoption of guidance for real-world evidence. Real-world data cannot be a substitute to randomized clinical studies but can possibly augment the generated evidence.
- Published
- 2021
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