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| 동등성/비열등성 시험× | t-검정을 위한 검정력 분석× | |
|---|---|---|
| 분야≠ | 실험설계 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 1987 | 1969 |
| 창시자≠ | Schuirmann, D.J. / EMA regulatory framework | Jacob Cohen |
| 유형≠ | Parametric equivalence / non-inferiority test | Sample size determination |
| 원전≠ | Schuirmann, D.J. (1987). A Comparison of the Two One-Sided Tests Procedure and the Power Approach. Journal of Pharmacokinetics and Biopharmaceutics, 15(6), 657–680. link ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 |
| 별칭≠ | non-inferiority trial, bioequivalence study, active-control trial, Denklik ve Üstünlük Olmayan Çalışma (Equivalence / Non-Inferiority) | t-test power analysis, sample size calculation for t-test, Güç Analizi — t-Testi |
| 관련≠ | 6 | 5 |
| 요약≠ | An equivalence or non-inferiority trial is a clinical study design that tests whether a new intervention is clinically equivalent to, or no worse than, an established standard by a pre-specified margin. Codified in Schuirmann's 1987 Two One-Sided Tests (TOST) framework and embedded in EMA and FDA regulatory guidance, this design is the regulatory standard for generic drug approval and medical device testing. | 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. |
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