Three Arguments for Absolute Outcome Measures
Open accessAbstract
Data from medical research are typically summarized with various types of outcome measures. We present three arguments in favor of absolute over relative outcome measures. The first argument is from cognitive bias: relative measures promote the reference class fallacy and the overestimation of treatment effectiveness. The second argument is decision-theoretic: absolute measures are superior to relative measures for making a decision between interventions. The third argument is causal: interpreted as measures of causal strength, absolute measures satisfy a set of desirable properties, but relative measures do not. Absolute outcome measures outperform relative measures on all counts.
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Framing vaccine efficacy as a relative measure can lead to cognitive bias that overestimates outcome effects.
“Framing vaccine efficacy as a relative measure can lead to cognitive bias that overestimates outcome effects [11].”
From Relative risk reduction: Misinformative measure in clinical trials and COVID-19 vaccine efficacy (2022) · cites this paper as [11]
In the Argument from Cognitive Bias (§3; page not supplied), the paper says relative outcome measures can prompt a reference-class fallacy and overestimation of medical intervention effects. Its examples concern drugs and other treatments, not vaccines specifically, so the vaccine-specific application is not established by our copy. (LLM, conf=0.82)
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Relative risk reduction: Misinformative measure in clinical trials and COVID-19 vaccine efficacy
Brown, R. B. (2022) · Dialogues in Health · doi:10.1016/j.dialog.2022.100074 · Cited by 11 · see citing works
“Arguing in favor of absolute outcome measures in clinical trials, Sprenger and Stegenga [11] point out that "relative outcome measures are neither necessary nor suf fi - cient for choosing between two interventions."”
- Authors
- Sprenger, J., & Stegenga, J.
- Venue
- Philosophy of Science
- Publisher
- Cambridge University Press (CUP)
- Published
- 2017-12
- Volume
- 84
- Issue
- 5
- Pages
- 840-852
- ISSN
- 0031-8248 (ISSN-L)
- 1539-767X
- OpenAlex
- W2586297728
- Cited by
- 36
- References
- 0
- Citations by year
- 2026: 3 · 2025: 15 · 2024: 1 · 2023: 4 · 2022: 6 · 2021: 1 · 2020: 3 · 2019: 2 · 2018: 1
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Philosophy of Science (publishedVersion)
- Research portal (Tilburg University) (publishedVersion)
- PhilSci-Archive (University of Pittsburgh) (acceptedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- H2020 European Research Council (640638)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- cognitive biases, causal strength, intervention comparison, medical research reporting
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