Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Jaribio la Msalaba la Mikono Mingi× | Jaribio la mikono mingi× | |
|---|---|---|
| Nyanja | Muundo wa Majaribio | Muundo wa Majaribio |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | Mid-20th century; multi-arm extensions formalized by 1970s–1980s | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| Mwanzilishi≠ | Developed from early crossover trial methodology (Williams 1949; Cochran & Cox 1957) | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| Aina≠ | Within-subject experimental design with multiple treatment arms | Experimental design |
| Chanzo asilia≠ | Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1584883869 | 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 | multi-arm crossover trial, multi-period multi-treatment crossover, CMAT, multi-treatment crossover experiment | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | A crossover multi-arm experiment is a within-subject experimental design in which each participant receives three or more treatments (arms) across successive periods, with random assignment to sequence. Because every participant experiences all arms, the design eliminates between-subject variability from treatment comparisons, dramatically increasing statistical power for a given sample size. It is widely used in clinical pharmacology, psychology, agriculture, and behavioral research. | A multi-arm experiment simultaneously compares three or more treatment or intervention conditions — each called an arm — against a shared control or against one another. By testing multiple alternatives in a single study, it yields more information per participant than running separate two-group experiments sequentially, while controlling the overall Type I error rate through pre-specified comparison strategies. |
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