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Latent variable mixture modelling and individual treatment prediction.

Rob Saunders1, Joshua E J Buckman1, Stephen Pilling1

  • 1Research Department of Clinical, Educational and Health Psychology, University College London, Gower Street, London, WC1E 7HB, UK.

Behaviour Research and Therapy
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Summary

This study used latent variable mixture modelling to create patient profiles for predicting psychological treatment outcomes. Different patient profiles showed varying recovery rates, with some benefiting more from high-intensity therapies or cognitive behavioral therapy (CBT).

Keywords:
IAPTLatent profile analysisPrecision medicinePsychotherapyTreatment outcomes

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Area of Science:

  • Psychiatry and Mental Health
  • Computational Statistics
  • Health Services Research

Background:

  • Personalised medicine in mental health often uses variable-centered approaches.
  • Person-centered approaches identifying patient profiles can predict a range of outcomes.
  • Latent variable mixture modelling offers a person-centered approach to patient profiling.

Purpose of the Study:

  • To discuss and demonstrate the use of latent variable mixture modelling for patient profiling.
  • To predict outcomes from psychological treatments using a patient profiling algorithm.
  • To analyze routinely collected patient data for treatment response patterns.

Main Methods:

  • Latent variable mixture modelling was applied to a large dataset of 44,905 patients.
  • A patient profiling algorithm was developed using routinely collected data.
  • Outcomes including reliable recovery, improvement, and deterioration were analyzed across identified profiles.

Main Results:

  • Eight distinct patient profiles emerged with consistent patterns of recovery, improvement, and deterioration over time.
  • High-intensity therapies showed higher odds of recovery or improvement in specific profiles compared to low-intensity treatments.
  • Cognitive Behavioral Therapy (CBT) was associated with higher reliable recovery rates than counseling in three patient profiles.

Conclusions:

  • Latent variable mixture modelling provides a valuable tool for identifying patient profiles to predict psychological treatment outcomes.
  • Treatment recommendations can be tailored based on patient profiles, optimizing therapy selection.
  • Further development and application of these person-centered approaches can enhance personalized mental healthcare.