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El tamaño efectivo previo de la muestra (ESS) es crucial para el préstamo de datos externos bayesianos. Este estudio amplía la definición del ESS de la relación de información local esperada (ELIR) a la escala del efecto del tratamiento, abordando una brecha metodológica clave para mejorar el diseño del ensayo.

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Área de la Ciencia:

  • Estadísticas biológicas
  • Ensayos clínicos
  • Inferencia Bayesiana

Sus antecedentes:

  • El préstamo de datos externos bayesianos se utiliza cada vez más en ensayos clínicos.
  • El tamaño exacto de la muestra efectiva previa (ESS, por sus siglas en inglés) es fundamental para el control de la información prestada.
  • Los métodos existentes del ESS se centran principalmente en el control de los préstamos, no en las escalas de efecto del tratamiento.

Objetivo del estudio:

  • Extender la definición del ESS de la relación de información local esperada (ELIR) a la escala del efecto del tratamiento.
  • Proporcionar un marco general y derivar el ESS para diversos parámetros y medidas del efecto del tratamiento.
  • Evaluar la propiedad de predicción de la consistencia del ESS ELIR propuesto.

Principales métodos:

  • Ampliación de la definición del ESS para el coeficiente de información local esperado (ELIR).
  • Derivación del ESS para varios tipos de puntos finales y medidas del efecto del tratamiento.
  • Evaluación de la consistencia predictiva para las diferentes combinaciones de puntos finales y efectos del tratamiento.

Principales resultados:

  • La definición de ELIR ESS se extendió con éxito a la escala del efecto del tratamiento.
  • Se derivaron fórmulas para evaluaciones previas de la eficacia del tratamiento para múltiples tipos de puntos finales y efectos del tratamiento.
  • La consistencia predictiva se mantuvo sólo para la diferencia entre dos parámetros normales.

Conclusiones:

  • Los métodos desarrollados cubren la brecha en el cálculo de la ESS previa en la escala del efecto del tratamiento.
  • Los hallazgos ponen de relieve la importancia de tener en cuenta los tipos de puntos finales y efectos del tratamiento al aplicar ELIR ESS.
  • Las implementaciones de R están disponibles para facilitar la aplicación de estos nuevos métodos de ESS en la práctica.