Recommendations for the use of propensity score methods in multiple sclerosis research. (August 2022)

• Record Type: Journal Article
• Authors: Simoneau, Gabrielle; Pellegrini, Fabio; Debray, Thomas PA; Rouette, Julie; Muñoz, Johanna; Platt, Robert W.; Petkau, John; Bohn, Justin; Shen, Changyu; de Moor, Carl; Karim, Mohammad Ehsanul
• Is Part Of: Multiple sclerosis. Volume 28:Number 9(2022)
• Page Start: 1467
• Page End: 1480
• Publication Date: 2022-08
• Subjects: Comparative effectiveness -- covariate selection -- inverse probability of treatment weighting -- observational study -- propensity score matching -- real-world data; Central nervous system -- Diseases -- Periodicals; Myelin sheath -- Diseases -- Periodicals; Inflammation -- Periodicals; Multiple sclerosis -- Periodicals; Central Nervous System Diseases -- Periodicals; Demyelinating Diseases -- Periodicals; Inflammation -- Periodicals; Multiple Sclerosis -- Periodicals; Système nerveux central -- Maladies -- Périodiques; Gaine de myéline -- Maladies -- Périodiques; Inflammation (Pathologie) -- Périodiques; Sclérose en plaques -- Périodiques; Electronic journals; 616.834005
• DOI: 10.1177/13524585221085733
• Languages: English
• ISSNs: 1352-4585

Abstract

Background: With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies. Objectives: To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research. Methods: We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice. Results: Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses. Conclusion: This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods.