Head-to-head drug comparisons in multiple sclerosis: Urgent action needed

Area of Science:

• Neurology
• Clinical Pharmacology
• Epidemiology

Background:

• Disease-modifying therapies are transforming multiple sclerosis (MS) natural history.
• Current clinical trial data are insufficient for personalized treatment algorithms in MS.
• Accurate treatment selection requires extensive head-to-head trials, which are often infeasible.

Purpose of the Study:

• To analyze the benefits and biases of alternative strategies for comparing multiple sclerosis (MS) treatments.
• To explore methods for reliable head-to-head drug comparisons in the absence of randomized controlled trials.

Main Methods:

• Review of strategies alternative to head-to-head trials for comparing MS treatments.
• Analysis of observational designs, including large multicenter prospective cohorts ('big MS data') and network meta-analyses.
• Consideration of biases inherent in these alternative study designs and the value of smaller, single-center cohorts.

Main Results:

• Observational designs and network meta-analyses provide valuable insights into relative treatment efficacy for multiple sclerosis (MS).
• These methods are susceptible to biases, necessitating confirmation in diverse study populations.
• Smaller, single-center cohorts can help minimize certain biases present in larger datasets.

Conclusions:

• No single alternative strategy perfectly replaces head-to-head trials for multiple sclerosis (MS) drug comparison.
• Combining data from large observational studies, network meta-analyses, and smaller cohorts offers a robust approach.
• A hybrid approach integrating multiple study types is proposed to achieve reliable head-to-head drug comparisons for MS management.