Francesca Ieva
- Associate Head of Research centre, Health Data Science
- Associate Professor of Statistics at MOX - Modeling and Scientific Computing laboratory, Department of Mathematics, Politecnico di Milano, Health Data Science
- Group Leader, Di Angelantonio & Ieva Group
Francesca Ieva è Associate Head dell’Health Data Science Centre di Human Technopole e docente di Statistica al Politecnico di Milano. Ha conseguito il dottorato di ricerca in Modelli e metodi matematici per l’ingegneria nel 2012. La sua ricerca si concentra sull’apprendimento statistico in ambito biomedico e sullo sviluppo di modelli avanzati per l’integrazione di dati clinici complessi, per informare le previsioni nel processo decisionale clinico e per supportare la medicina di precisione e le politiche di precisione.
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Pubblicazioni
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08/2020 - Biometrical Journal
Dynamic monitoring of the effects of adherence to medication on survival in heart failure patients: A joint modeling approach exploiting time-varying covariates
Adherence to medication is the process by which patients take their drugs as prescribed, and represents an issue in pharmacoepidemiological studies. Poor adherence is often associated with adverse health conditions and outcomes, especially in case of chronic diseases such as heart failure (HF). This turns out in an increased request for health care services, and […]
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06/2020 - BMC Health Services Research
Evaluating the effect of healthcare providers on the clinical path of heart failure patients through a semi-Markov, multi-state model
Background Investigating similarities and differences among healthcare providers, on the basis of patient healthcare experience, is of interest for policy making. Availability of high quality, routine health databases allows a more detailed analysis of performance across multiple outcomes, but requires appropriate statistical methodology. Methods Motivated by analysis of a clinical administrative database of 42,871 Heart […]
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11/2019 - Scientific Reports
Comparing methods for comparing networks
With the impressive growth of available data and the flexibility of network modelling, the problem of devising effective quantitative methods for the comparison of networks arises. Plenty of such methods have been designed to accomplish this task: most of them deal with undirected and unweighted networks only, but a few are capable of handling directed […]
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12/2018 - Biostatistics
Nonparametric frailty Cox models for hierarchical time-to-event data
We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax […]
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