Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Analýza silných strán založená na simulácii (silné stránky Monte Carlo)× | Bayesovská analýza sily (garancia)× | |
|---|---|---|
| Odbor | Štatistika | Štatistika |
| Rodina | Hypothesis test | Hypothesis test |
| Rok vzniku≠ | 2011 | 1986 |
| Tvorca≠ | Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension | Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005) |
| Typ≠ | Simulation-based (Monte Carlo) | Bayesian sample size determination |
| Pôvodný zdroj≠ | Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94. DOI ↗ | O'Hagan, A., Stevens, J.W. & Campbell, M.J. (2005). Assurance in Clinical Trial Design. Pharmaceutical Statistics, 4(3), 187–201. DOI ↗ |
| Ďalšie názvy | Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power) | assurance, bayesian sample size determination, bayesian assurance, Bayesian Güç Analizi (Assurance / Bayesian Sample Size) |
| Príbuzné≠ | 6 | 3 |
| Zhrnutie≠ | 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. | 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? |
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