Descripción del proyecto
Métodos estadísticos para tratamientos sanitarios complejos
Los tratamientos sanitarios son cada vez más complejos, lo que requiere herramientas estadísticas avanzadas para su análisis y guía. Los tratamientos complejos incluyen interacciones durante plazos prolongados, donde las medidas pasadas afectan al proceso de toma de decisiones, lo que significa que la dimensión temporal debe incluirse en los modelos estadísticos. La investigación comparativa de la eficacia (CER, por sus siglas en inglés) ha surgido recientemente como un nuevo elemento de la toma de decisiones en la asistencia sanitaria, diseñada para analizar la eficacia de diferentes tratamientos. En el proyecto Dynamic CER, financiado por las Acciones Marie Skłodowska-Curie, se desarrollarán métodos estadísticos innovadores para estudiar la dinámica temporal de tratamiento sanitarios complejos para la investigación primaria y la síntesis de pruebas. Una innovadora herramienta de metaanálisis de redes permitirá a los expertos supervisar y actualizar de forma dinámica los resultados de los metaanálisis de redes disponibles.
Objetivo
Comparative effectiveness research (CER) has recently emerged as a key component of health care decision-making, specifically designed to provide evidence of the effectiveness of different health care treatments. The latter are becoming increasingly complex, and there is now intense interest in deploying advanced statistical tools to study and guide the development of such complex systems. Complex interventions might also involve interactions through time, where actions in the past affect the future decision making context. In this case, the temporal dimension should be taken into account into the statistical model, yielding in turn more precise guidelines for health care decision-making. However, current CER methodologies are not well-suited to understand such complex systems or characterise their future behaviour. Statistical methods based on dynamic modelling are therefore needed to advance progress of the state-of-the art of CER research. In particular, this fellowship will tackle the problem by developing novel statistical methodologies for the study of the temporal dynamics of complex health care interventions, both for primary research and evidence synthesis. For primary research, the fellowship will explore the emerging case of pervasive and technology-based interventions, which are by nature dynamic. For evidence synthesis - which is the procedure of summarising evidence from different primary studies of a specific health condition - the innovative tool of network meta-analysis will be deployed and an extension for dynamically monitoring and updating the results of existing network meta-analyses will be developed.
Ámbito científico
Programa(s)
Régimen de financiación
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
75006 Paris
Francia