Bayesian Hierarchical Methods for Surveillance of Cervical Dystonia Treatments

Abstract

Cervical dystonia, a debilitating neurological disorder marked by involuntary muscle contractions and chronic pain, presents significant treatment challenges despite advances in botulinum toxin therapy. While botulinum toxin type B has emerged as one of the leading treatments, comparative efficacy across doses and the influence of demographic factors for personalized medicine remain understudied. This study aimed to: (1) compare the efficacy of different botulinum toxin type B doses using Bayesian methods, (2) evaluate demographic and clinical factors affecting treatment response, and (3) establish a probabilistic framework for personalized cervical dystonia management. We analyzed data from a multicenter randomized controlled trial involving 109 patients assigned to placebo, 5,000 units, or 10,000 units of botulinum toxin type B groups. The primary outcome was the Toronto Western Spasmodic Torticollis Rating Scale measured over 16 weeks. Bayesian hierarchical modeling assessed treatment effects while accounting for patient heterogeneity. Lower botulinum toxin type B doses (5,000 units) showed greater overall Toronto Western Spasmodic Torticollis Rating Scale score reductions (treatment effect: -2.39, 95% Probability Interval: -4.10 to -0.70). Male patients demonstrated better responses (5.2% greater improvement) than female patients. Substantial between-patient variability and site-specific effects were observed, highlighting the need for personalized protocols. The study confirms botulinum toxin type B's dose-dependent efficacy while identifying key modifiable factors in treatment response. Bayesian methods provided nuanced insights into uncertainty and heterogeneity, paving the way for personalized medicine in cervical dystonia management.