Errors-in-variables Modeling of Personalized Treatment-Response Trajectories
Abstract
Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications. However, learning the impact of a treatment on a continuous temporal response, when the covariates suffer extensively from measurement error and even the timing of the treatments is uncertain, has not been addressed. We introduce a novel data-driven method that can estimate treatment-response trajectories in this challenging scenario. We model personalized treatment-response curves as a combination of parametric response functions, hierarchically sharing information across individuals, and a sparse Gaussian process for the baseline trend. Importantly, our model considers measurement error not only in treatment covariates, but also in treatment times, a problem which arises in practice for example when treatment information is based on self-reporting. In a challenging and timely problem of estimating the impact of diet on continuous blood glucose measurements, our model leads to significant improvements in estimation accuracy and prediction.
Cite
@article{arxiv.1906.03989,
title = {Errors-in-variables Modeling of Personalized Treatment-Response Trajectories},
author = {Guangyi Zhang and Reza Ashrafi and Anne Juuti and Kirsi Pietiläinen and Pekka Marttinen},
journal= {arXiv preprint arXiv:1906.03989},
year = {2019}
}