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

TL;DR

This paper explores the ethical implications of using hold-out sets in clinical prediction models, emphasizing principles like beneficence, autonomy, and justice, and offers practical guidance for their implementation.

Contribution

It provides a comprehensive ethical analysis of hold-out set use in clinical models, including case discussions and recommendations for researchers.

Findings

Ethical principles guide hold-out set implementation.

Differences between hold-out sets and RCTs are analyzed.

Practical recommendations for researchers are provided.

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.