Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise de Potência para Estudos de Sobrevivência× | Análise de Poder para o teste t× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família | Hypothesis test | Hypothesis test |
| Ano de origem≠ | 1981 | 1969 |
| Autor original≠ | — | Jacob Cohen |
| Tipo≠ | Sample size determination for survival outcomes | Sample size determination |
| Fonte seminal≠ | Schoenfeld, D. A. (1981). The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika, 68(1), 316–319. DOI ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 |
| Outros nomes≠ | log-rank power analysis, cox regression power analysis, survival power analysis, Sağkalım Analizi Güç Analizi | t-test power analysis, sample size calculation for t-test, Güç Analizi — t-Testi |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | Power analysis for survival studies determines how many participants — and how many observed events — are required so that a log-rank test or Cox regression has a sufficient probability of detecting a clinically meaningful difference in survival between groups. The foundational formulas were derived by Schoenfeld (1981) and Lachin (1981) and remain the standard approach in clinical trial planning. | Power analysis for the t-test is a sample size planning procedure that determines how many participants are required to detect a mean difference of a given magnitude with acceptable probability. Formalised by Jacob Cohen in his 1969 and 1988 editions of Statistical Power Analysis for the Behavioral Sciences, it links four quantities — effect size (Cohen's d), significance level (α), statistical power (1 − β), and sample size — so that fixing any three allows calculation of the fourth. |
| ScholarGateConjunto de dados ↗ |
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