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Métodos de aleatorización mendeliana para la inferencia causal: estimandos, identificación e inferencia

Minhao Yao1, Anqi Wang2, Xihao Li3,4

  • 1Centre for Biomedical Data Science, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.

Statistics in medicine
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Resumen
Este resumen es generado por máquina.

La aleatorización mendeliana (AM) es una herramienta poderosa para inferir efectos causales en la investigación de la salud. Esta revisión cubre sistemáticamente los métodos de AM, los desafíos como los instrumentos inválidos y la orientación práctica para científicos aplicados.

Palabras clave:
aleatorización mendelianaUK Biobankgenómica causalinferencia causalvariables instrumentalesdatos ómicosconfusión no medida

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Área de la Ciencia:

  • Investigación Biomédica
  • Salud Pública
  • Epidemiología Genética

Sus antecedentes:

  • La aleatorización mendeliana (AM) utiliza variantes genéticas como variables instrumentales para establecer relaciones causales entre exposiciones y resultados de salud.
  • La AM ofrece un enfoque cuasi-experimental para superar la confusión y la causalidad inversa inherentes a los estudios observacionales.
  • A pesar de su utilidad, la AM enfrenta obstáculos metodológicos, incluidos instrumentos inválidos o débiles y estructuras de datos complejas.

Objetivo del estudio:

  • Proporcionar una revisión tutorial sistemática de los métodos de aleatorización mendeliana (AM) para la inferencia causal.
  • Aclarar la interpretación causal, comparar los diseños de estudio y ofrecer orientación práctica para los investigadores.
  • Cubrir desafíos como instrumentos inválidos y avances recientes para datos ómicos.

Principales métodos:

  • Descripción general sistemática de las metodologías de AM para la inferencia causal.
  • Discusión de estrategias para detectar y corregir instrumentos inválidos y débiles.
  • Integración de diseños de datos basados en la población frente a basados en la familia y de nivel individual frente a nivel de resumen.

Principales resultados:

  • Comparación de los diseños de AM de una muestra frente a dos muestras y sus limitaciones.
  • Resumen de los avances metodológicos recientes para escenarios complejos (por ejemplo, muchos instrumentos débiles, datos ómicos).
  • Aplicaciones ilustrativas utilizando datos del mundo real, incluidos estudios del UK Biobank y de la enfermedad de Alzheimer.

Conclusiones:

  • Esta revisión sirve como referencia tutorial para metodólogos y científicos aplicados en inferencia causal.
  • Enfatiza preguntas causales bien definidas y la aplicación práctica de los métodos de AM.
  • El contenido tiene como objetivo mejorar la aplicación rigurosa de la AM en la investigación biomédica y de salud pública.