方法对比
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| 顺序分析（分组顺序设计）× | 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≠ | 1977 | 2011 |
| 提出者≠ | P. C. O'Brien & T. R. Fleming; P. C. Pocock | Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension |
| 类型≠ | Sequential / adaptive hypothesis test | Simulation-based (Monte Carlo) |
| 开创性文献≠ | O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556. 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 ↗ |
| 别名 | sequential testing, group sequential design, interim analysis, Sıralı Analiz (Sequential Testing / Group Sequential Design) | Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power) |
| 相关≠ | 5 | 6 |
| 摘要≠ | Sequential analysis is a framework for conducting hypothesis tests with pre-planned interim looks at accumulating data, allowing a study to stop early for efficacy or futility while controlling the overall Type I error rate. The group sequential approach was formalised by Pocock (1977) and O'Brien and Fleming (1979), and remains the standard for confirmatory clinical trials and rigorous A/B experiments. | 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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