Treatment Preferences of Neurologists in Generalized Myasthenia Gravis: A Conjoint Analysis Study

Generalized myasthenia gravis (gMG) is a chronic autoimmune neuromuscular disorder for which treatment decision-making is increasingly complex due to the emergence of biologic targeted therapies. This study aimed to evaluate neurologists' treatment preferences in acetylcholine receptor seropositive gMG using conjoint analysis to simulate real-world clinical decision-making.

We conducted a cross-sectional, web-based study among neurologists involved in the management of gMG in collaboration with the Spanish Society of Neurology. Participants were presented with eight hypothetical treatment scenarios comprising five key attributes: intensity of improvement, onset of action, duration of effect, adverse events, and route/frequency of administration. Utility values and importance of each attribute were estimated using ordinary least squares regression. Demographic, professional, and behavioral characteristics were assessed to explore variability in preference patterns.

A total of 149 neurologists participated in the study. The mean age was 39.0 (SD 9.4) years, and 54.4% were male. Participants had a mean of 9.9 years (8.4) of experience treating patients with MG. The most valued treatment attribute was intensity of improvement (mean relative importance: 38.6%), followed by onset of action (21.5%) and duration of effect (17.4%). At the individual level, route and frequency of administration accounted for up to 21.9% of decision weight in some participants. Preferences were consistent across neuromuscular specialists and general neurologists but differed based on empathy and conscientiousness. Model fit was robust (Pearson's R = 1.000, p < 0.001).

Neurologists treating gMG placed the highest value on rapid and clinically meaningful symptom improvement, consistent with the therapeutic goals of emerging targeted therapies. However, substantial heterogeneity in preferences, especially regarding administration burden, emphasizes the need for individualized approaches and shared decision-making.