Ethical considerations of use of hold-out sets in clinical prediction model management
<|reference_start|>Ethical considerations of use of hold-out sets in clinical prediction model management: 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.<|reference_end|>
@article{chislett2024ethical,
    title={Ethical considerations of use of hold-out sets in clinical prediction
  model management},
    author={Louis Chislett, Louis JM Aslett, Alisha R Davies, Catalina A Vallejos,
  James Liley},
    journal={arXiv preprint arXiv:2406.03161},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03161},
    primaryClass={cs.LG cs.CY}
}