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Diseños óptimos para modelos de supervivencia en tiempo discreto con riesgos competitivos

XiaoDong Zhou1, YunJuan Wang2, RongXian Yue3

  • 1School of Statistics and Data Science, Shanghai University of International Business and Economics, Shanghai, 201620, China.

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Este estudio introduce diseños óptimos para ensayos de supervivencia aleatorizados con múltiples eventos de competencia. La asignación de grupos iguales es generalmente la mejor para ensayos de tiempo hasta el evento en tiempo discreto con riesgos competitivos.

Palabras clave:
Riesgos competitivosModelo de supervivencia en tiempo discretoEstudio longitudinalModelo paramétrico de riesgos competitivosEfectos del tratamiento dependientes del tiempo

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Área de la Ciencia:

  • Bioestadística
  • Diseño de Ensayos Clínicos
  • Análisis de Supervivencia

Sus antecedentes:

  • La investigación sobre el diseño de ensayos controlados aleatorizados (ECA) a menudo pasa por alto múltiples puntos finales de competencia.
  • Muchos ensayos clínicos enfrentan desafíos con múltiples eventos objetivo, careciendo de estrategias de diseño óptimo.
  • La literatura estadística existente no ha abordado sistemáticamente los diseños óptimos para ensayos de supervivencia con eventos de competencia.

Objetivo del estudio:

  • Desarrollar metodologías de diseño para ensayos aleatorizados de tiempo hasta el evento en tiempo discreto con puntos finales de competencia.
  • Abordar la brecha en las estrategias de diseño óptimo para ensayos con múltiples eventos de competencia.
  • Identificar diseños óptimos para estimar los efectos del tratamiento en entornos de ensayos tan complejos.

Principales métodos:

  • Se derivó la matriz de información de Fisher para el modelo de supervivencia en tiempo discreto (DTSM) transformando los datos en respuestas multivariadas.
  • Se introdujo un criterio de diseño óptimo generalizado basado en costos para identificar diseños óptimos.
  • Se asumió un modelo paramétrico de riesgos competitivos para el proceso de supervivencia subyacente.

Principales resultados:

  • Los esquemas óptimos de asignación de tratamientos están significativamente influenciados por los valores de los parámetros en el modelo de riesgos competitivos.
  • Se demostró que la asignación igual de sujetos es generalmente favorable en ensayos DTSM de dos brazos con riesgos competitivos.
  • Se identificaron excepciones en las que la asignación igual puede no ser óptima, específicamente cuando las tasas de peligro son bajas.

Conclusiones:

  • La metodología desarrollada proporciona estrategias de diseño óptimo para ensayos de tiempo hasta el evento en tiempo discreto con puntos finales de competencia.
  • Los hallazgos ofrecen orientación práctica para el diseño de ensayos clínicos con riesgos múltiples y competitivos.
  • El estudio destaca la importancia de considerar los eventos de competencia en las estrategias de diseño y asignación de ensayos.