Functional feature construction for individualized treatment regimes.

TitleFunctional feature construction for individualized treatment regimes.
Publication TypeJournal Article
Year of Publication2017
AuthorsLaber, Eric B., and Ana-Maria Staicu
JournalJ Am Stat Assoc
Date Published2017

Evidence-based personalized medicine formalizes treatment selection as an individualized treatment regime that maps up-to-date patient information into the space of possible treatments. Available patient information may include static features such race, gender, family history, genetic and genomic information, as well as longitudinal information including the emergence of comorbidities, waxing and waning of symptoms, side-effect burden, and adherence. Dynamic information measured at multiple time points before treatment assignment should be included as input to the treatment regime. However, subject longitudinal measurements are typically sparse, irregularly spaced, noisy, and vary in number across subjects. Existing estimators for treatment regimes require equal information be measured on each subject and thus standard practice is to summarize longitudinal subject information into a scalar, ad hoc summary during data pre-processing. This reduction of the longitudinal information to a scalar feature precedes estimation of a treatment regime and is therefore not informed by subject outcomes, treatments, or covariates. Furthermore, we show that this reduction requires more stringent causal assumptions for consistent estimation than are necessary. We propose a data-driven method for constructing maximally prescriptive yet interpretable features that can be used with standard methods for estimating optimal treatment regimes. In our proposed framework, we treat the subject longitudinal information as a realization of a stochastic process observed with error at discrete time points. Functionals of this latent process are then combined with outcome models to estimate an optimal treatment regime. The proposed methodology requires weaker causal assumptions than -learning with an ad hoc scalar summary and is consistent for the optimal treatment regime.

Alternate JournalJ Am Stat Assoc
Original PublicationFunctional feature construction for individualized treatment regimes.
PubMed ID30416232
PubMed Central IDPMC6223315
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 DK108073 / DK / NIDDK NIH HHS / United States