Errors-in-variables modeling of personalized treatment-response trajectories

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openAccess

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Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2021-01

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en

Pages

8
201-208

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Volume 25, issue 1

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.

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Citation

Zhang, G, Alizadeh Ashrafi, R, Juuti, A, Pietiläinen, K & Marttinen, P 2021, ' Errors-in-variables modeling of personalized treatment-response trajectories ', IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 1, 9072524, pp. 201-208 . https://doi.org/10.1109/JBHI.2020.2987323