Methodological considerations when analysing and interpreting real-world data

Abstract In the absence of relevant data from randomized trials, nonexperimental studies are needed to estimate treatment effects on clinically meaningful outcomes. State-of-the-art study design is imperative for minimizing the potential for bias when using large healthcare databases (e.g. claims da...

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Bibliographic Details
Published in:Rheumatology (Oxford, England) England), 2020-01, Vol.59 (1), p.14-25
Main Authors: Stürmer, Til, Wang, Tiansheng, Golightly, Yvonne M, Keil, Alex, Lund, Jennifer L, Jonsson Funk, Michele
Format: Article
Language:English
Subjects:
Data Interpretation, Statistical
Databases, Factual
Humans
Pragmatic Clinical Trials as Topic - methods
Propensity Score
REAL WORLD DATA: special section
Research Design
Rheumatology
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Summary:Abstract In the absence of relevant data from randomized trials, nonexperimental studies are needed to estimate treatment effects on clinically meaningful outcomes. State-of-the-art study design is imperative for minimizing the potential for bias when using large healthcare databases (e.g. claims data, electronic health records, and product/disease registries). Critical design elements include new-users (begin follow-up at treatment initiation) reflecting hypothetical interventions and clear timelines, active-comparators (comparing treatment alternatives for the same indication), and consideration of induction and latent periods. Propensity scores can be used to balance measured covariates between treatment regimens and thus control for measured confounding. Immortal-time bias can be avoided by defining initiation of therapy and follow-up consistently between treatment groups. The aim of this manuscript is to provide a non-technical overview of study design issues and solutions and to highlight the importance of study design to minimize bias in nonexperimental studies using real-world data.
ISSN:1462-0324
1462-0332
DOI:10.1093/rheumatology/kez320