TY - JOUR
T1 - Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment
AU - Cohen, Jeffrey A.
AU - Trojano, Maria
AU - Mowry, Ellen M.
AU - Uitdehaag, Bernard M.J.
AU - Reingold, Stephen C.
AU - Marrie, Ruth Ann
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The International Advisory Committee on Clinical Trials in Multiple Sclerosis and the International Conference on Data Leveraging to Answer Key Clinical Questions in Multiple Sclerosis were supported by the National Multiple Sclerosis Society and the European Committee for Treatment and Research in Multiple Sclerosis.
Publisher Copyright:
© The Author(s), 2019.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - Multiple sclerosis
KW - observational studies
KW - pragmatic clinical trials
KW - real-world data
KW - real-world evidence
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U2 - 10.1177/1352458519892555
DO - 10.1177/1352458519892555
M3 - Article
C2 - 31778094
AN - SCOPUS:85076847150
SN - 1352-4585
VL - 26
SP - 23
EP - 37
JO - Multiple Sclerosis Journal
JF - Multiple Sclerosis Journal
IS - 1
ER -