Zemplényi Antal

Dr. Antal Zemplényi


RWD module subproject


In addition to randomized controlled trial (RCT) data, the use of real-world data (RWD) is becoming more and more important in generating real- world evidence (RWD) for market access and reimbursement decisions.


The aim of the project is to collect data from the electronic health records of the University of Pécs and make it suitable for research purposes, and for clinical and health economics analyses. In this project, we would like to conduct two case studies: 1) analysing the costs and effectiveness of the treatment alternatives of prostate cancer by developing a health-economic model; 2) evaluating the cost-effectiveness of the seizure prediction in drug-resistant epilepsy patients.


Chains of treatment epizodes and individual clinical pathways of patients will be explored. The RWD module uses primarily retrospective data. Sequential of semi-automated algorithms steps (Determination of health states – Selection of patients & treatment epizodes – Data Transformation – Deriving modell structure – Populating the modell with data) will be used to generate health states and transition probabilities for health-economic (e.g. Markov) models. The long-term follow-up of patients provides an opportunity to map real-life care practices and to explore the effectiveness of alternative treatment forms (such as radiotherapy, surgery, hormone therapy, chemotherapy, etc.), as well as the cost-effectiveness of procedures. Within the project, the individual genetic background of prostate cancer patients will be mapped using new generation sequencing techniques. The resulting genetic and gene expression patterns will serve as inputs for estimating cost-effectiveness on a personalized basis.

Participating units

University of Pécs Chancellery, Health Technology Assessment Unit

University of Pécs Szentágothai Research Centre, Big Data Research Group

University of Pécs Szentágothai Research Centre, Bioinformatics Research Group

University of Pécs Faculty of Pharmacy, Department of Pharmacoeconomics


Dr. Ádám Feldmann
Big Data Research Group

Dr. Attila Gyenesei
Bioinformatics Research Group

Miklós Hornyák
Big Data Research Group

Ernő Hupuczi
Big Data Research Group

Zsolt Kisander
Big Data Research Group

Sándor Kovács
Health Technology Assessment Unit

Boglárka Láng
Faculty of Pharmacy (student)

Gábor Vincze
Health Technology Assessment Unit

Dr. Antal Zemplényi
Health Technology Assessment Unit, Department of Pharmacoeconomics