The use and quality of reporting of propensity score methods in multiple sclerosis literature: A review. (August 2022)

• Record Type: Journal Article
• Authors: Karim, Mohammad Ehsanul; Pellegrini, Fabio; Platt, Robert W; Simoneau, Gabrielle; Rouette, Julie; de Moor, Carl
• Is Part Of: Multiple sclerosis. Volume 28:Number 9(2022)
• Page Start: 1317
• Page End: 1323
• Publication Date: 2022-08
• Subjects: Comparative effectiveness -- causal inference -- disease-modifying therapies -- multiple sclerosis -- observational study -- propensity score; 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: 10.1177/1352458520972557
• Languages: English
• ISSNs: 1352-4585

Abstract

Background: Propensity score (PS) analyses are increasingly used in multiple sclerosis (MS) research, largely owing to the greater availability of large observational cohorts and registry databases. Objective: To evaluate the use and quality of reporting of PS methods in the recent MS literature. Methods: We searched the PubMed database for articles published between January 2013 and July 2019. We restricted the search to comparative effectiveness studies of two disease-modifying therapies. Results: Thirty-nine studies were included in the review, with most studies (62%) published within the past 3 years. All studies reported the list of covariates used for the PS model, but only 21% of studies mentioned how those covariates were selected. Most studies used PS matching (72%), followed by PS adjustment (18%), weighting (15%), and stratification (3%), with some overlap. Most studies using matching or weighting reported checking post-PS covariate imbalance (91%), although about 45% of these studies relied on p values from various statistical tests. Only 25% of studies using matching reported calculating robust standard errors for the PS analyses. Conclusions: The quality of reporting of PS methods in the MS literature is sub-optimal in general, and in some cases, inappropriate methods are used.