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Project

Statistical models in the evaluation of predictors of therapeutic success. Applications in Cancer and Covid-19

Innovative research in recent years has led to the discovery of many promising targeted agents including immunotherapy such as monoclonal antibodies and vaccines. The varying mechanisms of action introduced by these novel agents challenge researchers to reconsider whether the conventional efficacy analyses adequately address the new mechanisms of action of these therapies [1]. There are two important aspect that need to be considered: 1) a change in survival curves, characterized by a stable plateau at the end of the curve with heavy censoring in the tail, suggesting the existence of a subgroup of long-term survivors and 2) the transition from a traditional clinical practice model to precision medicine therapies that require the identification of key predictors to select the optimal treatment for individual patients. These topics of research are included in this PhD project and applied to evaluate the therapeutic success for immunotherapy in advanced lung cancer and to identify pre-treatment biomarkers for cancer and covid-19.

Date:25 Oct 2022 →  Today
Keywords:Infections diseases, biomarkers of treatment success, information theory, statistical modelling
Disciplines:Biostatistics
Project type:PhD project