השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| ניסוי פקטוריאלי חלקי מקובץ-אקראי× | ניסוי מרובה זרועות× | |
|---|---|---|
| תחום | תכנון ניסויים | תכנון ניסויים |
| משפחה | Process / pipeline | Process / pipeline |
| שנת המקור≠ | 1950s (fractional factorial); 1980s-1990s (cluster-randomized extensions) | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| הוגה השיטה≠ | Box, Hunter & Hunter (fractional factorial foundations); Murray & colleagues (group-randomized trial methodology) | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| סוג≠ | Experimental design (compound) | Experimental design |
| מקור מכונן≠ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 | 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 ↗ |
| כינויים | CR-FFE, cluster-randomized fractional factorial design, group-randomized fractional factorial trial, CRFFD | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| קשורות | 5 | 5 |
| תקציר≠ | A cluster-randomized fractional factorial experiment combines two design principles: randomization is applied to intact groups (clusters such as schools, clinics, or communities) rather than individuals, and only a carefully chosen fraction of all possible factor-level combinations is tested. This pairing makes it practical to screen or evaluate multiple intervention components simultaneously in settings where individual randomization is infeasible, while keeping the number of required clusters manageable. | 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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