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Statistical modelling of time-varying covariates for survival data

  • M. Spreafico
woensdag 12 oktober 2022
Rapenburg 73
2311 GJ Leiden


  • Prof.dr. M. Fiocco


Time-varying covariates are a challenging task in clinical research, as they represent dynamic patterns which affect patient's health status and disease progression. This dissertation focuses on developing new mathematical and statistical methods to properly represent time-varying covariates and model them within the context of time-to-event analysis. The main purpose is to enrich the knowledge available for modelling survival with relevant features related to the time-varying processes of interest.

The efforts of this work address the complexities of both (i) developing adequate dynamic characterizations of the processes under study (i.e., the representation problem) and (ii) identifying and quantifying the association between time-varying processes and patient survival  (i.e., the time-to-event modelling problem). In both cases, the main issue is dealing with complex data sources while taking into account the nature of the processes and managing the complex trade-off between clinical interpretability and mathematical formulation.

By solving the aforementioned statistical complexities, this work is not only impacting the community of researchers in mathematics and statistics, but it aims at providing useful tools to support doctors and clinicians in their work. All research topics are motivated by specific clinical questions aimed at gaining insights into personalised treatments for cardiological and oncological patients.
In conclusion, the development of these novel methodologies may represent a significant step forward in the definition of new customized  and flexible monitoring tools, which could then be applied to the study of different pathologies characterised by complex data sources.


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