Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Jaribio la awali la mikono mingi× | Jaribio la Mikono Mingi linalobadilika× | |
|---|---|---|
| Nyanja | Muundo wa Majaribio | Muundo wa Majaribio |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1990s–2000s | 2000s–2010s (MAMS framework formalized c. 2003–2011) |
| Mwanzilishi≠ | Evolved from clinical trial methodology; consolidated in the 1990s–2000s | Patrick Royston, Mahesh Parmar, and colleagues (multi-arm multi-stage framework); further developed by James Wason, Thomas Jaki and others |
| Aina≠ | Experimental design (pilot/feasibility) | Experimental design |
| Chanzo asilia≠ | Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L., & Goldsmith, C. H. (2010). A tutorial on pilot studies: The what, why and how. BMC Medical Research Methodology, 10(1), 1. DOI ↗ | 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 ↗ |
| Majina mbadala | pilot multi-arm trial, feasibility multi-arm study, pilot parallel-arm experiment, preliminary multi-arm experiment | MAMS design, multi-arm adaptive trial, adaptive platform trial, response-adaptive multi-arm experiment |
| Zinazohusiana≠ | 6 | 3 |
| Muhtasari≠ | A pilot multi-arm experiment is a small-scale preliminary trial that tests the feasibility, logistics, and parameter estimates needed to plan a full-scale multi-arm study. It simultaneously evaluates two or more active treatment arms alongside a control, providing early evidence on recruitment rates, retention, protocol adherence, variability, and likely effect sizes before committing to a resource-intensive definitive experiment. | 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. |
| ScholarGateSeti ya data ↗ |
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