Errors-in-variables Modeling of Personalized Treatment-Response Trajectories

TL;DR

This paper introduces a novel data-driven method for estimating personalized treatment-response trajectories in the presence of measurement errors in covariates and treatment timing, improving accuracy in complex real-world scenarios.

Contribution

The paper presents a new modeling approach that accounts for measurement errors in both covariates and treatment times, combining parametric response functions and sparse Gaussian processes.

Findings

Significant improvements in estimation accuracy for blood glucose response.

Effective handling of measurement errors in treatment timing and covariates.

Enhanced prediction of treatment effects in personalized medicine.

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.