Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Jaribio la Kimatendo la Kimatibabu× | Ubunifu wa Majaribio Unaojirekebisha× | |
|---|---|---|
| Nyanja | Utafiti wa Kliniki | Utafiti wa Kliniki |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2009-2015 | 1990s-2000s |
| Mwanzilishi≠ | Thorpe et al. (2009); PRECIS framework developed by international consortia | Stephen Pocock, Christopher Jennison, and statistical methodologists; FDA formalized guidance 2019 |
| Aina | Research Design | Research Design |
| Chanzo asilia≠ | Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Tugwell, P. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. CMAJ, 180(10), E47–E57. DOI ↗ | Pocock, S. J. (2005). Current issues in the design and interpretation of clinical trials. BMJ, 330(7500), 1118–1121. link ↗ |
| Majina mbadala≠ | pragmatic trial, real-world trial, effectiveness trial, PRECIS-2 | adaptive trial, adaptive design, response-adaptive randomization, RAR |
| Zinazohusiana≠ | 4 | 1 |
| Muhtasari≠ | A pragmatic trial is designed to evaluate the real-world effectiveness of an intervention in typical clinical settings with diverse, representative patients, minimal exclusion criteria, and clinically relevant outcomes. Developed by Thorpe and colleagues (2009) and formalized via the PRECIS-2 framework (2015), pragmatic trials bridge the gap between explanatory efficacy trials (conducted in controlled research settings) and implementation science, answering the question 'Does this work in actual clinical practice?' rather than 'Can this work under ideal conditions?' | An adaptive trial design allows pre-specified modifications to the trial based on interim data—such as sample size re-estimation, stopping for futility or efficacy, dropping ineffective arms, or shifting randomization ratios toward better-performing treatments. Developed systematically in the 1990s–2000s by statisticians like Pocock and Jennison, and formalized by the FDA in 2019, adaptive designs accelerate drug development, reduce exposure to ineffective treatments, and improve efficiency without inflating false-positive rates when properly executed. |
| ScholarGateSeti ya data ↗ |
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