Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Adaptīvs daudzroku eksperiments× | Adaptīvs eksperiments× | |
|---|---|---|
| Nozare | Eksperimentu plānošana | Eksperimentu plānošana |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2000s–2010s (MAMS framework formalized c. 2003–2011) | 1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s |
| Autors≠ | Patrick Royston, Mahesh Parmar, and colleagues (multi-arm multi-stage framework); further developed by James Wason, Thomas Jaki and others | Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others |
| Tips≠ | Experimental design | Experimental research design |
| Pirmavots≠ | Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256. DOI ↗ | Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761 |
| Citi nosaukumi | MAMS design, multi-arm adaptive trial, adaptive platform trial, response-adaptive multi-arm experiment | adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization |
| Saistītās≠ | 3 | 5 |
| Kopsavilkums≠ | An adaptive multi-arm experiment simultaneously evaluates several treatment conditions against a common control and modifies the trial in real time based on accumulating data — dropping ineffective arms early, reallocating participants toward promising ones, or adjusting sample sizes — all while controlling error rates. The approach maximizes information gained per participant and reduces the time and cost required to identify effective treatments relative to running sequential separate trials. | An adaptive experiment is an experimental design in which pre-specified rules allow the protocol to be modified — such as reallocating participants to better-performing arms, stopping early for efficacy or futility, or changing sample size — based on accumulating interim data, while maintaining statistical validity. Adaptive designs are widely used in clinical trials, behavioural economics, and online platform testing to improve efficiency and ethics without sacrificing inferential rigour. |
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