Ethical considerations of use of hold-out sets in clinical prediction model management

• DOI: 10.1007/s43681-024-00561-z
• Publication Date: 2024-09-10T10:03:40Z
• Authors: Louis Chislett; Louis J. M. Aslett; Alisha R. Davies; Catalina A. Vallejos; James Liley
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computers and Society; Computers and Society (cs.CY); Machine Learning (cs.LG)

Abstract Clinical prediction models are statistical or machine learning models used to quantify the risk of a certain health outcome using patient data. These can then inform potential interventions on patients, causing an effect called performative prediction: predictions inform interventions which influence the outcome they were trying to predict, leading to a potential underestimation of risk in some patients if a model is updated on this data. One suggested resolution to this is the use of hold-out sets, in which a set of patients do not receive model derived risk scores, such that a model can be safely retrained. We present an overview of clinical and research ethics regarding potential implementation of hold-out sets for clinical prediction models in health settings. We focus on the ethical principles of beneficence, non-maleficence, autonomy and justice. We also discuss informed consent, clinical equipoise, and truth-telling. We present illustrative cases of potential hold-out set implementations and discuss statistical issues arising from different hold-out set sampling methods. We also discuss differences between hold-out sets and randomised control trials, in terms of ethics and statistical issues. Finally, we give practical recommendations for researchers interested in the use hold-out sets for clinical prediction models.