The risks of risk assessment: causal blind spots when using prediction models for treatment decisions
Abstract
Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who have already received the treatment the prediction model is meant to inform. Special attention to the causal role of those earlier treatments is required when interpreting the resulting predictions. We identify 'causal blind spots' in three common approaches to handling treatment when developing a prediction model: including treatment as a predictor, restricting to individuals taking a certain treatment, and ignoring treatment. Through several real examples, we illustrate how the risks obtained from models developed using such approaches may be misinterpreted and can lead to misinformed decision-making. Our discussion covers issues attributable to confounding, selection, mediation and changes in treatment protocols over time. We advocate for an extension of guidelines for the development, reporting and evaluation of prediction models to avoid such misinterpretations. Developers must ensure that the intended target population for the model, and the treatment conditions under which predictions hold, are clearly communicated. When prediction models are intended to inform treatment decisions, they need to provide estimates of risk under the specific treatment (or intervention) options being considered, known as 'prediction under interventions'. Next to suitable data, this requires causal reasoning and causal inference techniques during model development and evaluation. Being clear about what a given prediction model can and cannot be used for prevents misinformed treatment decisions and thereby prevents potential harm to patients.
Authors 15
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Nan van Geloven Aachen
Leiden University Medical Center
Affiliation as printed
( ) Leiden University Medical Center , Leiden , The Netherlands (
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London School of Hygiene & Tropical Medicine
Affiliation as printed
) London School of Hygiene and Tropical Medicine , London , United Kingdom (
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Delft University of Technology
Affiliation as printed
) Delft University of Technology , Delft , The Netherlands (
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University of Cambridge · University of Manchester
Affiliation as printed
) THIS Institute , University of Cambridge , United Kingdom (
) University of Manchester , Manchester , United Kingdom (
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University Medical Center Utrecht
Affiliation as printed
) University Medical Center Utrecht , Utrecht , The Netherlands (
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Affiliation as printed
) University of Amsterdam , Amsterdam , The Netherlands. (
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University of Washington · Ghent University
Affiliation as printed
) Ghent University , Ghent , Belgium (
) University of Washington , Seattle , United States (
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Affiliation as printed
) Utrecht University , Utrecht , The Netherlands (
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Hein Putter Aachen
Leiden University Medical Center
Affiliation as printed
( ) Leiden University Medical Center , Leiden , The Netherlands (
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Affiliation as printed
) University of Manchester , Manchester , United Kingdom (
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Hochschule Bremen · University of Bremen · Leibniz Institute for Prevention Research and Epidemiology - BIPS
Affiliation as printed
) Department of Mathematics and Computer Science , University of Bremen , Germany
) Leibniz Institute for Prevention Research and Epidemiology -BIPS , Bremen , Germany
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