Public Health Expertise has extensive experience in developing simulation models in various therapeutic areas and is recognised as an expert team, able to interpret and implement real-life data, clinical-trial based evidence, and clinical experts’ opinions into state-of-the-art mathematical models.
Mathematical modelling and simulation is an approach that aims to mimic real systems. The conceptualisation and translation into simulation algorithms of studied processes allows evolution prediction and the exploration of their reaction to changes in silico.
Modelling methods have become a standard for scientific research in many areas, including physics, biology, economics and life-sciences. Many healthcare payers and health technology assessment agencies rely on health-economic models to assess the potential value of new treatments and devices by extrapolating clinical trials efficacy results into real-life costs and effects.
For every model, we aim to best conceptualize a disease’s natural history, thus allowing extrapolations of the effects and impacts of clinical innovation on patients’ lives, disease burden and clinical landscape.
Due to their specific efficacy, safety and resistance profile, new combined antiretroviral therapies could potentially be administered before the results of genotype profiling, allowing for treatment of patients immediately after
In the context of continuous CF care improvement and emergence of novel etiologic treatments, we developed a patient-level simulation based on historical patients’ data accounting for the complex interactions between
While voluntary individual screening for cervical cancer is already well established in France, the country will soon begin to implement organised screening with ambitious screening rates and cervical cancer incidence
Clinical and business development optimization for a multi-treatment response-stratification tool in oncology
We explored the value of a currently developed multi-treatment patient-stratification technique that allows individual-based oncologic treatment selection through the determination of the most potent treatment available (including non-approved treatments) based