方法对比

	贝叶斯功效分析（保证值）	基于仿真的功效分析（蒙特卡洛功效）
领域	统计学	统计学
方法族	Hypothesis test	Hypothesis test
起源年份	1986	2011
提出者	Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005)	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension
类型	Bayesian sample size determination	Simulation-based (Monte Carlo)
开创性文献	O'Hagan, A., Stevens, J.W. & Campbell, M.J. (2005). Assurance in Clinical Trial Design. Pharmaceutical Statistics, 4(3), 187–201.	Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94.
别名	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)
相关	3	6
摘要	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.