Measuring Individual Benefits of Medical Treatments Using Longitudinal Hospital Data with Non-Ignorable Missing Responses Caused by Patient Discharge: Application to the Study of Benefits of Pain Management Post Spinal Fusion

• Autores: Xuan Zhang, Nikos Pantazis, Jose de Leon, Francisco J. Diaz
• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 45, Nº. 2, 2022, págs. 275-300
• Idioma: inglés
• DOI: 10.15446/rce.v45n2.101597
• Títulos paralelos:
  • Medición de los beneficios individuales de tratamientos médicos a partir de datos hospitalarios longitudinales con respuestas faltantes no ignorables causadas por la alta del paciente: Aplicación al estudio de los beneficios del tratamiento contra el dolor después de una fusión espinal
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• Resumen
  • español

    Resumen Los registros de salud electrónicos (RSE) suministran recursos valiosos para estudios longitudinales y para comprender los factores de riesgo asociados con pobres resultados clínicos. Sin embargo, estos podrían no contener seguimientos completos, y los datos faltantes podrían no ser al azar, debido a que el alta hospitalaria puede depender en parte de resultados clínicos esperados pero no registrados que ocurren después de dar de alta al paciente. Esta ausencia de datos no ignorables requiere métodos apropiados de análisis. Aquí estamos interesados en medir y analizar beneficios individuales de tratamientos médicos en pacientes consignados en bases de datos RSE. Proponemos un método para predecir beneficios individuales el cual maneja los datos faltantes debidos al alta hospitalaria. La respuesta clínica longitudinal de interés se modela junto con el tiempo de estadía en el hospital usando un modelo conjunto de efectos mixtos, y los beneficios individuales se predicen por medio de un enfoque frecuentista: el enfoque Bayesiano empírico. Nuestro enfoque es ilustrado evaluando los beneficios individuales del tratamiendo para el dolor en pacientes que fueron sometidos a cirugía de fusión espinal. Aquí examinamos la evolución de los beneficios individuales a través del tiempo mediante el cálculo de los percentiles muéstrales de los predictores de Bayes empíricos de los beneficios individuales. También comparamos estos percentiles con percentiles calculados mediante un enfoque Monte Cario. Los resultados mostraron que los predictores de Bayes empíricos de beneficios individuales no sólo permiten examinar beneficios en pacientes específicos sino que también reflejan confiablemente las tendencias poblacionales globales.

  • English

    Abstract Electronic health records (EHR) provide valuable resources for longitudinal studies and understanding risk factors associated with poor clinical outcomes. However, they may not contain complete follow-ups, and the missing data may not be at random since hospital discharge may depend in part on expected but unrecorded clinical outcomes that occur after patient discharge. These non-ignorable missing data requires appropriate analysis methods. Here, we are interested in measuring and analyzing individual treatment benefits of medical treatments in patients recorded in EHR databases. We present a method for predicting individual benefits that handles non-ignorable missingness due to hospital discharge. The longitudinal clinical outcome of interest is modeled simultaneously with the hospital length of stay using a joint mixed-effects model, and individual benefits are predicted through a frequentist approach: the empirical Bayesian approach. We illustrate our approach by assessing individual pain management benefits to patients who underwent spinal fusion surgery. By calculating sample percentiles of empirical Bayes predictors of individual benefits, we examine the evolution of individual benefits over time. We additionally compare these percentiles with percentiles calculated with a Monte Carlo approach. We showed that empirical Bayes predictors of individual benefits do not only allow examining benefits in specific patients but also reflect overall population trends reliably.

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