Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Analiză de putere bayesiană (Asigurare)× | Analiza puterii bazată pe simulare (Puterea Monte Carlo)× | |
|---|---|---|
| Domeniu | Statistică | Statistică |
| Familie | Hypothesis test | Hypothesis test |
| Anul apariției≠ | 1986 | 2011 |
| Autorul original≠ | Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005) | Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension |
| Tip≠ | Bayesian sample size determination | Simulation-based (Monte Carlo) |
| Sursa seminală≠ | O'Hagan, A., Stevens, J.W. & Campbell, M.J. (2005). Assurance in Clinical Trial Design. Pharmaceutical Statistics, 4(3), 187–201. DOI ↗ | Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94. DOI ↗ |
| Denumiri alternative | assurance, bayesian sample size determination, bayesian assurance, Bayesian Güç Analizi (Assurance / Bayesian Sample Size) | Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power) |
| Înrudite≠ | 3 | 6 |
| Rezumat≠ | Bayesian power analysis — also called assurance — is a sample size determination method that replaces the frequentist notion of power with a probability-weighted average over a prior distribution on the effect size. First formalised by Spiegelhalter and Freedman (1986) and further developed by O'Hagan, Stevens and Campbell (2005), it answers the question: given our current uncertainty about the true effect, what sample size gives us a high overall probability of obtaining a statistically significant result? | Simulation-based power analysis estimates the statistical power and required sample size of a study by repeating a full analysis pipeline thousands of times on artificially generated data. Because it relies on Monte Carlo simulation rather than closed-form equations, it is applicable to designs — mixed models, complex measurement structures, non-standard outcomes — where analytical power formulas do not exist. The approach was systematically described for applied research by Arnold et al. in 2011, and the mixed-model implementation via the SIMR package was formalised by Green and MacLeod in 2016. |
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