From algorithms to action: improving patient care requires causality

TL;DR

This paper emphasizes the importance of incorporating causality into outcome prediction models in cancer research to ensure they support safe and effective treatment decisions, highlighting current shortcomings and proposing solutions.

Contribution

It clarifies why existing models may be harmful despite accuracy and offers guidance on developing causally sound models for clinical decision making.

Findings

Current prediction models lack causal considerations.

Models validated without causality can cause harm.

Guidelines need to incorporate causal validation methods.

Abstract

In cancer research there is much interest in building and validating outcome predicting outcomes to support treatment decisions. However, because most outcome prediction models are developed and validated without regard to the causal aspects of treatment decision making, many published outcome prediction models may cause harm when used for decision making, despite being found accurate in validation studies. Guidelines on prediction model validation and the checklist for risk model endorsement by the American Joint Committee on Cancer do not protect against prediction models that are accurate during development and validation but harmful when used for decision making. We explain why this is the case and how to build and validate models that are useful for decision making.