Vertaile menetelmiä
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| Simulaatiopohjainen voima-analyysi (Monte Carlo -voima)× | Bayesiläinen voima-analyysi (varmuus)× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Hypothesis test | Hypothesis test |
| Syntyvuosi≠ | 2011 | 1986 |
| Kehittäjä≠ | Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension | Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005) |
| Tyyppi≠ | Simulation-based (Monte Carlo) | Bayesian sample size determination |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet | 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) |
| Liittyvät≠ | 6 | 3 |
| Tiivistelmä≠ | 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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