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

Guangyi Zhang, Reza Alizadeh Ashrafi, Anne Juuti, Kirsi Pietiläinen, Pekka Marttinen

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
17 Downloads (Pure)


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.
Original languageEnglish
Article number9072524
Pages (from-to)201-208
Number of pages8
Issue number1
Early online date20 Apr 2020
Publication statusPublished - Jan 2021
MoE publication typeA1 Journal article-refereed


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