Abstrakti
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.
Alkuperäiskieli | Englanti |
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Artikkeli | 9072524 |
Sivut | 201-208 |
Sivumäärä | 8 |
Julkaisu | IEEE Journal of Biomedical and Health Informatics |
Vuosikerta | 25 |
Numero | 1 |
Varhainen verkossa julkaisun päivämäärä | 20 huhtik. 2020 |
DOI - pysyväislinkit | |
Tila | Julkaistu - tammik. 2021 |
OKM-julkaisutyyppi | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä |