APPLYING LATENT CLASS CLUSTER ANALYSIS AND DATA MINING METHODS TO IDENTIFY CLASSES OF CHRONIC FATIGUE SYNDROME PATIENTS THAT ARE PREDICTIVE OF TREATMENT SUCCESS

Student thesis: Doctoral ThesisDoctor of Philosophy

Abstract

Chronic fatigue syndrome (CFS) is a condition characterised by chronic disabling fatigue and other symptoms that currently cannot be explained by any underlying medical condition. It is estimated that 250,000 of the UK population suffer from CFS. Although clinical trials support the effectiveness of cognitive behaviour therapy in terms of fatigue and physical functioning, the success rate for individual patients is modest. Patients vary in their response and little is known about which factors predict or moderate their treatment outcomes.

Identifying moderators of treatment effect is typically done in a regression-based approach by assessing interactions between clinical and other baseline variables and treatment groups. Moderators typically are of small effect size and prediction models often show poor accuracy in predicting outcomes. My project took a different approach to identify classes of patients with similar baseline characteristics. I compared a model-based cluster analysis method of Latent Class Analysis against an automatic distribution-free machine learning algorithm, Self-Organising Maps. The classes identified were tested for predictive usefulness of treatment effects. Characteristics of the classes can be used to inform clinicians about the types of individuals who benefit from specific treatments.

The suitability of the clustering methods was compared using data on CFS patients in two datasets, a large clinical cohort study and a randomised clinical trial. Using the trial data, I also compared the performance of the clustering techniques against a computer-intensive statistical learning penalised regression method which provides predictions without clustering. The LASSO regression model was also developed to identify moderators of treatment success. These comparisons allow the assessment of the potential advantages of clustering approaches and their capability of identifying complex relationships between variables in the prediction of treatment success in CFS above the regression-based approach.
Date of Award1 Jul 2022
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorDaniel Stahl (Supervisor), Trudie Chalder (Supervisor) & Kimberley Goldsmith (Supervisor)

Cite this

'