Tree-based methods for individualized treatment regimes.

TitleTree-based methods for individualized treatment regimes.
Publication TypeJournal Article
Year of Publication2015
AuthorsLaber, E B., and Y Q. Zhao
Date Published2015

Individualized treatment rules recommend treatments on the basis of individual patient characteristics. A high-quality treatment rule can produce better patient outcomes, lower costs and less treatment burden. If a treatment rule learned from data is to be used to inform clinical practice or provide scientific insight, it is crucial that it be interpretable; clinicians may be unwilling to implement models they do not understand, and black-box models may not be useful for guiding future research. The canonical example of an interpretable prediction model is a decision tree. We propose a method for estimating an optimal individualized treatment rule within the class of rules that are representable as decision trees. The class of rules we consider is interpretable but expressive. A novel feature of this problem is that the learning task is unsupervised, as the optimal treatment for each patient is unknown and must be estimated. The proposed method applies to both categorical and continuous treatments and produces favourable marginal mean outcomes in simulation experiments. We illustrate it using data from a study of major depressive disorder.

Alternate JournalBiometrika
Original PublicationTree-based methods for individualized treatment regimes.
PubMed ID26893526
PubMed Central IDPMC4755313
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States