Errors-in-Variables Modeling of Personalized Treatment-Response Trajectories
Open Access
- 20 April 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Biomedical and Health Informatics
- Vol. 25 (1), 201-208
- https://doi.org/10.1109/jbhi.2020.2987323
Abstract
Estimating the impact of a treatment on a given response is needed in many biomedical applications. However, methodology is lacking for the case when the response is a continuous temporal curve, treatment covariates suffer extensively from measurement error, and even the exact timing of the treatments is unknown. We introduce a novel method for 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 accounts for errors not only in treatment covariates, but also in treatment timings, a problem arising in practice for example when data on treatments are based on user self-reporting. We validate our model with simulated and real patient data, and show that in a challenging application of estimating the impact of diet on continuous blood glucose measurements, accounting for measurement error significantly improves estimation and prediction accuracy.Funding Information
- Business Finland (884/31/2018)
- Academy of Finland (286607, 294015)
- Suomen Lääketieteen Säätiö
- Academy of Finland (314383, 266286)
- Academy of Finland Centre of Excellence in Research on Mitochondria, Metabolism and Disease (272376)
- Suomen Lääketieteen Säätiö
- Gyllenberg Foundation, the Novo Nordisk Foundation (NNF17OC0027232, NNF10OC1013354)
- Diabetestutkimussäätiö
- Sydäntutkimussäätiö
- University of Helsinki and Helsinki University Hospital
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