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Latent variable mixture modelling and individual treatment prediction

Saunders, R; Buckman, JEJ; Pilling, S; (2020) Latent variable mixture modelling and individual treatment prediction. Behaviour Research and Therapy , 124 , Article 103505. 10.1016/j.brat.2019.103505. Green open access

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Abstract

Understanding which groups of patients are more or less likely to benefit from specific treatments has important implications for healthcare. Many personalised medicine approaches in mental health employ variable-centred approaches to predicting treatment response, yet person-centred approaches that identify clinical profiles of patients can provide information on the likelihood of a range of important outcomes. In this paper, we discuss the use of latent variable mixture modelling and demonstrate its use in the application of a patient profiling algorithm using routinely collected patient data to predict outcomes from psychological treatments. This validation study analysed data from two services, which included n = 44,905 patients entering treatment. There were different patterns of reliable recovery, improvement and clinical deterioration from therapy, across the eight profiles which were consistent over time. Outcomes varied between different types of therapy within the profiles: there were significantly higher odds of reliable recovery with High Intensity therapies in two profiles (32.5% of patients) and of reliable improvement in three profiles (32.2% of patients) compared with Low Intensity treatments. In three profiles (37.4% of patients) reliable recovery was significantly more likely if patients had CBT vs Counselling. The developments and potential application of latent variable mixture approaches are further discussed.

Type: Article
Title: Latent variable mixture modelling and individual treatment prediction
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.brat.2019.103505
Publisher version: https://doi.org/10.1016/j.brat.2019.103505
Language: English
Additional information: © 2019 The Authors. Published by Elsevier Ltd. Under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/).
Keywords: Latent profile analysis, Psychotherapy, IAPT, Precision medicine, Treatment outcomes
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Clinical, Edu and Hlth Psychology
URI: https://discovery.ucl.ac.uk/id/eprint/10085128
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