Carla Rognoni

Carla Rognoni is a Researcher in Health Economics & HTA within the Government, Health, and Not for Profit division of SDA Bocconi School of Management. She started collaborating with CERGAS Bocconi in 2008. At SDA Bocconi, Carla coordinates and actively works on various research projects in the field of health economics involving pharmacological treatments or medical devices with companies such as Abbvie, BD, Boston Scientific, Johnson & Johnson, Novartis, Sanofi, and many others. She frequently develops project proposals with a particular focus on patient involvement and the management of ethical aspects. Her research activities focus on a variety of topics related to Health Technology Assessment (HTA) in public health, clinical medicine, and EU projects (e.g., ERAPerMed PRESERVE, JACARDI, RESOLVE): economic evaluations, quality of life assessments, cost-effectiveness and budget impact analyses, decision models (including Markov process modeling). Carla is also involved in preparing dossiers for market access. She has published over 150 articles/abstracts on health economics topics, with over 70 indexed on PubMed (H-index 25), and authored several book chapters. Her works have been published in journals such as Value in Health, Pharmacoeconomics, Clinical Therapeutics, Clinical Drug Investigation, Lancet Oncology, Scientific Reports, Medical Decision Making, and many others. Carla serves as a reviewer for numerous international scientific journals and is a member of the Italian Health Economics Association (AIES) and the International Society for Pharmacoeconomics and Outcome Research (ISPOR). From 2016 to 2019, she was appointed as a member (expert in pharmacoeconomics) of the Drugs Commission at the IRCCS Policlinico San Matteo in Pavia, Italy. Carla holds a Master's degree in Computer Engineering and a Ph.D. in Bioengineering and Bioinformatics (cum laude) from the University of Pavia, based on research on decision analysis topics (cost-effectiveness analysis, cost-utility analysis, and Markov process modeling) in the prevention of sudden deaths caused by long QT syndrome.