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.