Methodological considerations when analysing and interpreting real-world data

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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.
Funding Information
  • National Institute on Aging (R01AG056479)
  • National Institutes of Health, Bethesda, MD, USA
  • National Institute on Aging (R01 AG056479, R01 CA174453, R01 HL118255, R21-HD080214)
  • Biostatistics, Epidemiology, and Research Design
  • North Carolina Translational and Clinical Sciences Institute (UL1TR002489)
  • Center for Pharmacoepidemiology
  • Department of Epidemiology, UNC

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