Podcast - SEASON 3
Episode 3
Common errors in interpreting survival curves
Kaplan-Meier curves are one of the most widely used tools in clinical research. They are also one of the most misinterpreted. In this episode, Dr. Adrián Mosquera examines the most common errors when reading survival curves: from being misled by visual geometry to misinterpreting the median, the hazard ratio, or a significant p-value.
The episode covers five key areas: the temporal shape of benefit, assumptions around censoring and data maturity, overly simplistic metric shortcuts, design and analysis biases, and cases where the Kaplan-Meier is simply not the right tool.
An episode for reading survival curves with sharper eyes and a clearer sense of what they hide.
The Expert
Adrián Mosquera Orgueira is a specialist in Hematology and Hemotherapy at the University Hospital of Santiago de Compostela and holds a Doctorate in Medicine from the University of Santiago de Compostela. He currently leads the Computational Hematology and Genomics Group (GrHeco-Xen) at the Santiago Health Research Institute (IDIS), focusing on the applications of genomics and artificial intelligence in the field of precision medicine. This line aims to improve the prediction of hematological cancer response to available treatments, as well as the development of personalized therapies for hematological cancer. He is the co-author of dozens of research papers in recent years, including publications in high-impact journals in the Hematology sector such as Leukemia, Blood Cancer Journal, Hemasphere, and Cancers. Additionally, he is currently involved in several research projects in the field of various hematological neoplasms. Dr. Mosquera combines this research activity with his clinical practice at CHUS and with the dissemination of scientific knowledge.
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