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Precision psychiatry: thinking beyond simple prediction models – enhancing causal predictions: commentary, Seyedsalehi et al

Published online by Cambridge University Press:  13 November 2025

Aida Seyedsalehi Open the ORCID record for Aida Seyedsalehi [Opens in a new window] ,
Giulio Scola Open the ORCID record for Giulio Scola [Opens in a new window]  and
Seena Fazel Open the ORCID record for Seena Fazel [Opens in a new window]
Aida Seyedsalehi
Affiliation:
Department of Psychiatry, University of Oxford, Oxford, UK
Giulio Scola
Affiliation:
Department of Psychiatry, University of Oxford, Oxford, UK
Seena Fazel*
Affiliation:
Department of Psychiatry, University of Oxford, Oxford, UK Oxford Health NHS Foundation Trust, Oxford, UK
*
Correspondence: Seena Fazel. Email: seena.fazel@psych.ox.ac.uk
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Abstract

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Keywords

Prognosisprediction modelsmodel performancecausal inferencevalidation

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Type
Commentary
Information
The British Journal of Psychiatry , First View , pp. 1 - 2
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal College of Psychiatrists

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References

Krishnadas, R, Leighton, SP, Jones, PB. Precision psychiatry: thinking beyond simple prediction models – enhancing causal predictions. Br J Psychiatry 2025; 226: 184–8.10.1192/bjp.2024.258CrossRefGoogle ScholarPubMed
Steyerberg, EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating 2nd ed. Springer, 2019.10.1007/978-3-030-16399-0CrossRefGoogle Scholar
van Geloven, N, Swanson, SA, Ramspek, CL, Luijken, K, van Diepen, M, Morris, TP, et al. Prediction meets causal inference: the role of treatment in clinical prediction models. Eur J Epidemiol 2020; 35: 619–30.10.1007/s10654-020-00636-1CrossRefGoogle ScholarPubMed
Whiting, D, Mallett, S, Lennox, B, Fazel, S. Assessing violence risk in first-episode psychosis: external validation, updating and net benefit of a prediction tool (OxMIV). BMJ Ment Health 2023; 26: e300634.10.1136/bmjment-2022-300634CrossRefGoogle ScholarPubMed
Fazel, S, Vazquez-Montes, MDLA, Molero, Y, Runeson, B, D’Onofrio, BM, Larsson, H, et al. Risk of death by suicide following self-harm presentations to healthcare: development and validation of a multivariable clinical prediction rule (OxSATS). BMJ Ment Health 2023; 26: e300673.10.1136/bmjment-2023-300673CrossRefGoogle ScholarPubMed
Hernán, MA, Robins, JM. Causal Inference: What If. Chapman & Hall/CRC, 2020.Google Scholar
Mansournia, MA, Etminan, M, Danaei, G, Kaufman, JS, Collins, G. Handling time varying confounding in observational research. BMJ 2017; 359: j4587.10.1136/bmj.j4587CrossRefGoogle ScholarPubMed
Keogh, RH, Geloven, NV. Prediction under interventions: evaluation of counterfactual performance using longitudinal observational data. Epidemiology 2024; 35: 329–39.10.1097/EDE.0000000000001713CrossRefGoogle ScholarPubMed
Arnold, KF, Davies, V, de Kamps, M, Tennant, PWG, Mbotwa, J, Gilthorpe, MS. Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning. Int J Epidemiol 2020; 49: 2074–82.10.1093/ije/dyaa049CrossRefGoogle Scholar

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