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sleev: Un paquete de R para la estimación semiparamétrica de verosimilitud con errores en las variables

Jiangmei Xiong1, Sarah C Lotspeich2, Joey B Sherrill3

  • 1Department of Biostatistics, Vanderbilt University Medical Center, USA.

Journal of open source software
|February 23, 2026
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Resumen
Este resumen es generado por máquina.

Este estudio presenta el paquete de R sleev para analizar datos biomédicos con errores de medición. Implementa eficientemente el estimador de máxima verosimilitud tamizada (SMLE) para estudios de dos fases con resultados o covariables propensos a errores.

Palabras clave:
R packagesemiparametric likelihooderrors-in-variablessieve maximum likelihood estimationtwo-phase studiesbiomedical datameasurement errorSMLEcovariatesoutcomes

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Área de la Ciencia:

  • Investigación biomédica
  • Metodología estadística
  • Ciencia de datos

Sus antecedentes:

  • Los datos recopilados rutinariamente en la investigación biomédica a menudo contienen errores de medición en los resultados o covariables.
  • Los diseños de estudios de dos fases son comunes, donde solo se valida una submuestra de datos.
  • El análisis de datos propensos a errores requiere métodos estadísticos especializados.

Objetivo del estudio:

  • Abordar la necesidad de herramientas computacionalmente eficientes y fáciles de usar para analizar datos propensos a errores en estudios de dos fases.
  • Presentar el paquete de R `sleev` para implementar el estimador de máxima verosimilitud tamizada (SMLE).
  • Facilitar la inferencia semiparamétrica basada en la verosimilitud para resultados y covariables binarios y continuos propensos a errores.

Principales métodos:

  • Se utilizó el enfoque del estimador de máxima verosimilitud tamizada (SMLE).
  • Se desarrolló el paquete de R `sleev` para implementar SMLE para estudios de dos fases.
  • El paquete maneja resultados y covariables binarios y continuos propensos a errores.

Principales resultados:

  • El paquete de R `sleev` proporciona una herramienta fácil de usar para aplicar SMLE.
  • Permite un análisis eficiente y robusto de datos complejos propensos a errores.
  • Soporta el análisis tanto para resultados binarios como continuos con errores de medición.

Conclusiones:

  • El paquete de R `sleev` llena eficazmente el vacío para analizar datos propensos a errores en estudios de dos fases.
  • Mejora la accesibilidad y eficiencia del uso de SMLE en la investigación biomédica.
  • La herramienta soporta una amplia gama de tipos de datos, incluyendo respuestas y covariables propensas a errores.