While clinical trials provide information on the effect of MS therapies in the short term, there is a need to understand their longer-term effects in real-world use, especially on disability in MS. Researchers now have excellent access to long-term clinical data on thousands of people living with MS through global registries such as MSBase. These provide an excellent resource to answer these questions, but they require complex analysis. This is especially so, because many of factors, such as age, time on therapy, etc, are not standardised like they are in a clinical trial.
Marginal structural models (MSM) are a type of statistical model that are used to estimate causal links in this kind of “observational data” in registries and therefore have been used to mimic clinical trials. However, there is a need to broaden the current scope of what can be addressed by MSM, to answer questions about the long-term effects of therapies, delayed effects of therapies and comparing multiple interventions.
The research team have developed this project to extend the present capabilities of MSM to include questions that cannot feasibly be answered in clinical trials.
Professor Tomas Kalincik and his team have successfully developed and refined analytical approaches to better understand how MS treatments affect long-term disability outcomes using real-world data. These methods extend beyond what is possible in traditional clinical trials, allowing researchers to compare multiple treatments and examine their long-term and delayed effects.
As part of the project, the team assembled and analysed a large international dataset from the MSBase registry, enabling comparison of seven different disease-modifying therapies (DMTs). They developed and implemented advanced statistical methods that allow multiple treatments to be compared simultaneously in a way that more closely reflects real-world clinical practice.
The research also addressed several important challenges when using real-world data. The team developed new approaches to improve the accuracy of treatment comparisons, including methods to correct for biases that can occur when people with MS enter studies at different timepoints and when treatment histories are incomplete. This ensures that treatment effects are estimated more reliably.
Using these methods, the study showed that starting high-efficacy treatment early after diagnosis leads to improved disease control and reduced disability over time. Importantly, this benefit was seen across different age groups, including both younger and older people with MS.
Overall, this project has created an extremely useful framework for comparing multiple MS treatments using real-world data. These findings provide valuable evidence to support more informed, data-driven treatment decisions by clinicians, with the potential to improve long-term outcomes and reduce disability for people living with MS.
Updated 31 March 2026
$247,924
2022
3 years
Past project
