Statistical methods for the identification and modelling of lifestyle factors related to Huntington’s Disease severity and progression

Markoulidakis, Andreas 2024. Statistical methods for the identification and modelling of lifestyle factors related to Huntington’s Disease severity and progression. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

Randomised controlled trials (RCTs) are considered the gold standard to estimate the effect of interventions on the progression of diseases. Observational studies are gaining traction to study the effect of modifiable factors on the progression and severity of rare diseases, as they are often easier and cheaper to implement than RCTs. Observational studies do not require tailored interventions; rather, they involve observing the behaviour of the participants and monitoring their progression. However, making inferences using data from observational studies can be challenging for several reasons, including imbalances in confounders between the control and treatment groups. In this thesis, I use observational data in an attempt to identify risk factors for Huntington’s disease (HD) severity and progression. HD is an inherited disorder that results in the death of brain cells and typically leads to death 15-20 years after clinical diagnosis. It is a rare disease (prevalence 4-8 per 100,000). I began by proposing a set of steps to make inferences using data from observational studies, controlling for confounding bias using Propensity Score and Balancing Weights (PSBW), and developing a web application (CoBWeb), which implements these steps in a user-friendly environment. Next, I used a well established simulation study to understand how the limited sample size (typical for rare diseases), the choice of baseline covariates for balancing could affect the ability to control for confounding bias. Finally, I applied these methods to the Enroll-HD dataset to investigate the effect of several modifiable lifestyle factors (e.g., use of antidepressant medication) on the progression of HD. Analysis of Enroll-HD data, using the proposed methodology, identified a potentially harmful effect of antidepressant medication on the progression of HD among those in the early stage of the disease.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Medicine
Date of First Compliant Deposit:	11 April 2024
Last Modified:	11 Apr 2024 09:00
URI:	https://orca.cardiff.ac.uk/id/eprint/167840

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