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Sensitivity Analysis in the Analysis of Real-World Data

In: Real-World Evidence in Medical Product Development

Author

Listed:
  • Yixin Fang

    (AbbVie, Data and Statistical Sciences)

  • Weili He

    (AbbVie, Data and Statistical Sciences)

Abstract

As defined in ICH E9(R1), sensitivity analysis is “a series of analyses conducted with the intent to explore the robustness of inferences from the main estimator to deviations from its underlying modeling assumptions and limitations in the data.” We start with an anatomy of potential assumptions behind an answerable research question. Then, we discuss how to conduct sensitivity analysis to explore the robustness of inferences to deviations from the following three sets of assumptions: (1) the set of causal identifiability assumptions behind the causal model, (2) the set of intercurrent events (ICE) assumptions behind the strategies of ICE handling, and (3) the set of statistical assumptions behind the estimation process of the estimand. We conclude the chapter by emphasizing the difference between sensitivity analysis and supplementary analysis, which is defined in ICH E9(R1) as “a general description for analyses that are conducted in addition to the main and sensitivity analysis with the intent to provide additional insights into the understanding of the treatment effect.”

Suggested Citation

  • Yixin Fang & Weili He, 2023. "Sensitivity Analysis in the Analysis of Real-World Data," Springer Books, in: Weili He & Yixin Fang & Hongwei Wang (ed.), Real-World Evidence in Medical Product Development, pages 271-287, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-26328-6_15
    DOI: 10.1007/978-3-031-26328-6_15
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