Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment

Abstract

Randomized controlled clinical trials and real-world observational studies provide complementary information but with different validity. Some clinical questions (disease behavior, prognosis, validation of outcome measures, comparative effectiveness, and long-term safety of therapies) are often better addressed using real-world data reflecting larger, more representative populations. Integration of disease history, clinician-reported outcomes, performance tests, and patient-reported outcome measures during patient encounters; imaging and biospecimen analyses; and data from wearable devices increase dataset utility. However, observational studies utilizing these data are susceptible to many potential sources of bias, creating barriers to acceptance by regulatory agencies and the medical community. Therefore, data standardization and validation within datasets, harmonization across datasets, and application of appropriate analysis methods are important considerations. We review approaches to improve the scope, quality, and analyses of real-world data to advance understanding of multiple sclerosis and its treatment, as an example of opportunities to better support patient care and research.

Multiple Sclerosis in the Campania Region (South Italy): Algorithm Validation and 2015–2017 Prevalence

“…Population-based studies can overcome these limitations. Datasets of routinely collected healthcare data, derived from both publicly-funded and private healthcare systems [13,14], provide standardised healthcare resource utilisation in the long-term (e.g., diagnoses, procedures, medications), and on fully representative populations [15]. As such, population-based studies can provide a detailed description of disease epidemiology, comorbidities and treatment pathways [15], also thanks to the linkage to clinical registries [16,17].…”

Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS)

“…The US MS PATHS centers are currently in various stages of SmartForm implementation, while we are coordinating with the EU MS PATHS centers to harmonize as much as possible. (8) Incorporating the voice of the patient: Patients have always been identified as a key stakeholder in MS PATHS. During the design phase, the investigators engaged a local patient advisory group, when available, on the MS PATHS concept.…”

“…The rigorous collection and analysis of real-world data may accelerate the development of personalized medicine in MS and address some of these gaps ( 3 – 5 ). Opportunities and challenges related to data pooling and data standardization in MS were recognized by early pioneers, beginning decades ago ( 6 , 7 ), and recent progress has been summarized ( 8 , 9 ). These efforts have ushered in an era of data standardization and pooling in an attempt to extend systematic learning beyond structured research protocols to more representative real-world populations.…”

The use and quality of reporting of propensity score methods in multiple sclerosis literature: A review

“…Most studies (87%) used logistic regression to estimate the PS, with 5% of studies resorting to statistical model selection. On average, seven covariates were used to construct the PS (range: [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. All studies reported a list of these variables, but only 21% of studies reported how this list was determined: of those, 75% used expert opinion and 25% used statistical tests.…”

“…The availability of various registries and large multicentre MS cohorts thus opened the opportunity to answer research questions that are impractical to investigate using RCTs. [3][4][5][6] The estimation of causal treatment effects in observational studies is challenging, mainly because treatment is not assigned at random. Hence, these sources can be prone to biases, such as selection bias and confounding by indication.…”