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Errors-in-Variables Modeling of Personalized Treatment-Response Trajectories
- Publication Year :
- 2021
-
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.
- Subjects :
- FOS: Computer and information sciences
Market research
hierarchical models
Computer Science - Machine Learning
Treatment-response trajectories
Informatics
Bayesian methods
wearable self-monitoring devices
Trajectory
Data models
Gaussian processes
Machine Learning (stat.ML)
time-series data
113 Computer and information sciences
Machine Learning (cs.LG)
Measurement errors
Mathematical model
Blood
Statistics - Machine Learning
errors-in-variables
3111 Biomedicine
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....eb6c06b392345e951ab2a951b72df058