Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise Sequencial (Delineamento Sequencial por Grupos)× | Análise de Poder Baseada em Simulação (Poder de Monte Carlo)× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família | Hypothesis test | Hypothesis test |
| Ano de origem≠ | 1977 | 2011 |
| Autor original≠ | P. C. O'Brien & T. R. Fleming; P. C. Pocock | Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension |
| Tipo≠ | Sequential / adaptive hypothesis test | Simulation-based (Monte Carlo) |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes | 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) |
| Relacionados≠ | 5 | 6 |
| Resumo≠ | 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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