Title: Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods,
Abstract: The optimal learner for prediction modeling varies depending on the
underlying data-generating distribution. Super Learner (SL) is a generic
ensemble learning algorithm that uses cross-validation to select among a
"library" of candidate prediction models. The SL is not restricted to a single
prediction model, but uses the strengths of a variety of learning algorithms to
adapt to different databases. While the SL has been shown to perform well in a
number of settings, it has not been thoroughly evaluated in large electronic
healthcare databases that are common in pharmacoepidemiology and comparative
effectiveness research. In this study, we applied and evaluated the performance
of the SL in its ability to predict treatment assignment using three electronic
healthcare databases. We considered a library of algorithms that consisted of
both nonparametric and parametric models. We also considered a novel strategy
for prediction modeling that combines the SL with the high-dimensional
propensity score (hdPS) variable selection algorithm. Predictive performance
was assessed using three metrics: the negative log-likelihood, area under the
curve (AUC), and time complexity. Results showed that the best individual
algorithm, in terms of predictive performance, varied across datasets. The SL
was able to adapt to the given dataset and optimize predictive performance
relative to any individual learner. Combining the SL with the hdPS was the most
consistent prediction method and may be promising for PS estimation and
prediction modeling in electronic healthcare databases.