Abstract
Suppose a doctor is interested in predicting the individual outcomes for a group of patients under two particular drug regimens at two time points in the future. She is therefore asking, what would happen to each of these patient’s future outcomes at these time points if I were to enforce drug regimen 1 or drug regimen 2? Which treatment will be better for the patients’ efficacy outcomes? Which treatment will be better for the patients’ safety outcomes? Prediction problems can be longitudinal in nature, generally, and we frequently wish to understand what the mean outcome of patients with certain characteristics would be months or years in the future. Often, this is under the setting where we would hypothetically assign a particular treatment “rule” to the patients.
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Rose, S., van der Laan, M.J. (2018). Sequential Super Learning. In: Targeted Learning in Data Science. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-65304-4_3
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