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

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 beneflts of medical treatments in patients recorded in EHR databases. We present a method for predicting individual beneflts 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 beneflts are predicted through a frequentist approach: the empirical Bayesian approach. We illustrate our approach by assessing individual pain management beneflts to patients who underwent spinal fusion surgery. By calculating sample percentiles of empirical Bayes predictors of individual beneflts, we examine the evolution of individual beneflts over time. We additionally compare these percentiles with percentiles calculated with a Monte Carlo approach. We showed that empirical Bayes predictors of individual beneflts do not only allow examining beneflts in speciflc patients but also reflect overall population trends reliably.