Application of the Bayesian network theory in clinical trial data: Severity shift in spasticity numeric rating scale...

Data digitization expands data collection opportunities, representing both a chance to understand interrelationships between variables and a challenge to identify the most appropriate clinical factors. Applications of causal inference techniques to clinical trial data is becoming very attractive, especially with the intent to provide insights into the relationships between baseline characteristics and outcomes. Graphical representations of model structures and conditional probabilities can be powerful tools to illustrate relationships in a high-dimensional data setting.

We review and apply Bayesian network theory to a clinical case study, presenting an analytical approach to investigating and visualizing causal relationships. We propose the use of the adherence score to compare data networks' patterns based on different variables' discretization. Data from adult patients with spasticity related to multiple sclerosis (MSS) from two randomized placebo-controlled clinical trials of nabiximols were used as analysis sets. The training and validation sets included 106 (53 treated, 53 placebo) and 155 (76 treated, 79 placebo) participants, respectively. The primary objective was to create a network and estimate the causal dependencies between participants' characteristics, changes in MSS severity as reflected by shifts in the patient-reported numeric rating scale (NRS), and changes in symptoms, functional abilities, and quality of life factors.

A causal network was identified between the key factors of assigned treatment, end of study spasticity NRS, and mental health/vitality subscales of the 36-Item Short Form Health Survey questionnaire (4 nodes and 3 edges; adherence score = 93%). In patients with mild spasticity, the impact of nabiximols on mental health or vitality subscales resulted in a probability ratio of 1.63. The decomposed mediation effect of spasticity NRS was observed through a mediation analysis between treatment and mental health (99.4%) or vitality (93.7%) subscales.

The use of innovative methods such as causal networks is highly encouraged to identify dependent relationships among key factors in clinical trial data and drive insights for additional research.