Marginal structural models (MSM), a type of statistical model, are used to estimate causal links in observational/cohort data and therefore have been used to mimic clinical trials. However, there is an unmet need to broaden the current scope of what can be addressed by MSM, such as long-term effects of interventions, delayed effects of interventions and comparing multiple interventions.
The research team have developed this project to extend the present capabilities of MSM to include studies beyond the capabilities of clinical trials. These methods will enable researchers to use real-life data to draw conclusions about long-term and delayed treatment effects and compare multiple therapies.
Professor Tomas Kalincik and his team have assembled and quality-checked a dataset from MSBase, an international database consisting of real-world data on people with MS, to allow them to compare seven disease modifying therapies (DMTs) over time. The team have adopted analytical methods and study design features to allow simultaneous analysis of multiple MS therapies.
Next, Professor Kalincik and his team will use two complementary methods to compare the effectiveness of treatments.
The information gained from this study will improve health outcomes in people living with MS by providing an objective comparison of multiple therapies to specialist healthcare providers. A more accurate, data-driven selection of MS therapies can lower the cost of specialist care by optimising treatment resource use and more effectively preventing future disability.
Diouf I, Malpas CB, Sharmin S, et al. Effectiveness of multiple disease modifying therapies in relapsing-remitting multiple sclerosis: causal inference to emulate a multi-arm randomised trial. J Neurol Neurosurg Psychiatry 2023,94:1004-11
Updated: 31 March 2025
$247,924
2022
3 years
Current project
