## Resumen

In randomized controlled clinical trials, treatment efficacy is considered as its capability to produce beneficial effects. Efficacy of an experimental treatment is usually established comparing its effectiveness respect to a placebo or comparing with the effect of an active control treatment. The first stage to determine effectiveness of a new treatment is placebo-controlled trial, and, as a second stage, a superiority trial will compare new treatment with a treatment uniformly accepted as a standard one. Sometimes the use of a placebo control is considered unethical; in such a case, a non-inferiority trial may be appropriate. In non-inferiority the new treatment dont need to be superior to a control, it is enough that it doesn't be unacceptably worse and additionally to be better than placebo. This assert rules the construction of non-inferiority hypothesis, which is based in a non-inferiority margin; in its determination is necessary to consider both statistical reasoning and clinical judgement. The possibility to claim incorrectly non-inferiority is one of the methodological flaws in non-inferiority studies and is related with the determination of non-inferiority margin. Two samples t-test is the statistical criteria to analyze non-inferiority hypothesis when mean value is the parameter to measure treatments efficacy. The objective of present report was to obtain and to characterize the p-value probability distribution for t-test in non-inferiority designs and to establish the relationship between this distribution and test's power function, as a result a characterization of the effect of sample size and non-inferiority margin over test sensibility was obtained, in addition an application of p-value distribution to meta-analysis was done introducing a criteria to analyze the effect of a particular study in results integration.

Idioma original | Español |
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Páginas (desde-hasta) | 259-265 |

Número de páginas | 7 |

Publicación | Investigacion Operacional |

Volumen | 38 |

N.º | 3 |

Estado | Publicada - 2017 |

## All Science Journal Classification (ASJC) codes

- Estadística y probabilidad
- Análisis numérico
- Matemáticas aplicadas