Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-23T16:58:01.250Z Has data issue: false hasContentIssue false
  • English
  • Français

Three Arguments for Absolute Outcome Measures

Published online by Cambridge University Press:  01 January 2022

Jan Sprenger  and
Jacob Stegenga
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Type
Evidence and Inference
Information
Philosophy of Science , Volume 84 , Issue 5 , December 2017 , pp. 840 - 852
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

The authors wish to thank Aaron Kenna, Clark Glymour, Felipe Romero, and the audience at PSA 2016 for helpful feedback and discussion. Research on this topic was financially supported by European Research Council Starting Investigator grant 640638 (Sprenger). The authors contributed equally to this article.

References

Bobbio, M., Demichelis, B., and Giustetto, G. 1994. “Completeness of Reporting Trial Results: Effect on Physicians’ Willingness to Prescribe.” Lancet 343 (8907): 1209–11.CrossRefGoogle Scholar
Cartwright, N. 1979. “Causal Laws and Effective Strategies.” Noûs 13:419–37.CrossRefGoogle Scholar
Deeks, J. 1998. “When Can Odds Ratios Mislead?” British Medical Journal 317:1155–56.CrossRefGoogle ScholarPubMed
Eells, E. 1991. Probabilistic Causality. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Fitelson, B., and Hitchcock, C. 2011. “Probabilistic Measures of Causal Strength.” In Causality in the Sciences, ed. Illari, P. M., Russo, F., and Williamson, J., 600–627. Oxford: Oxford University Press.CrossRefGoogle Scholar
Forrow, L., Taylor, W. C., and Arnold, R. M. 1992. “Absolutely Relative: How Research Results Are Summarized Can Affect Treatment Decisions.” American Journal of Medicine 92 (2): 121–24.CrossRefGoogle ScholarPubMed
Frick, M. H., et al. 1987. “Helsinki Heart Study: Primary-Prevention Trial with Gemfibrozil in Middle-Aged Men with Dyslipidemia; Safety of Treatment, Changes in Risk Factors, and Incidence of Coronary Heart Disease.” New England Journal of Medicine 317 (20): 1237–45.CrossRefGoogle ScholarPubMed
Good, I. J. 1961. “A Causal Calculus.” Pt. 1. British Journal for the Philosophy of Science 11:305–18.CrossRefGoogle Scholar
HPSCG (Heart Protection Study Collaborative Group). 2002. “MRC/BHF Heart Protection Study of Cholesterol Lowering with Simvastatin in 20,536 High-Risk Individuals: A Randomised Placebo-Controlled Trial.” Lancet 360 (9326): 7–22.CrossRefGoogle Scholar
Hux, Janet E., and Naylor, C. David. 1995. “Communicating the Benefits of Chronic Preventive Therapy: Does the Format of Efficacy Data Determine Patients’ Acceptance of Treatment?” Medical Decision Making 15:152–57.CrossRefGoogle ScholarPubMed
King, Nicholas B., Harper, Sam, and Young, Meredith E. 2012. “Use of Relative and Absolute Effect Measures in Reporting Health Inequalities: Structured Review.” BMJ 345. doi:10.1136/bmj.e5774.CrossRefGoogle Scholar
Malenka, David, Baron, John, Johansen, Sarah, Wahrenberger, Jon, and Ross, Jonathan. 1993. “The Framing Effect of Relative and Absolute Risk.” Journal of General Internal Medicine 8 (10): 543–48.CrossRefGoogle ScholarPubMed
Nex⊘e, J⊘rgen, Gyrd-Hansen, Dorte, Kragstrup, Jakob, Kristiansen, Ivar S⊘nb⊘, and Nielsen, Jesper Bo. 2002. “Danish GPs’ Perception of Disease Risk and Benefit of Prevention.” Family Practice 19 (1): 3–6.CrossRefGoogle Scholar
Northridge, M. E. 1995. “Public Health Methods-Attributable Risk as a Link between Causality and Public Health Action.” American Journal of Public Health 85:1202–4.CrossRefGoogle ScholarPubMed
Nurminen, Markku. 1995. “To Use or Not to Use the Odds Ratio in Epidemiologic Analyses.” European Journal of Epidemiology 11:365–71.CrossRefGoogle ScholarPubMed
Sorensen, L., Gyrd-Hansen, D., Kristiansen, I. S., Nexoe, J., and Nielsen, J. B. 2008. “Laypersons’ Understanding of Relative Risk Reductions: Randomised Cross-Sectional Study.” BMC Medical Informatics and Decision Making 8 (31). doi:10.1186/1472-6947-8-31.CrossRefGoogle ScholarPubMed
Sprenger, J. Forthcoming. “Foundations for a Probabilistic Theory of Causal Strength.” Philosophical Review.Google Scholar
Stegenga, Jacob. 2015. “Measuring Effectiveness.” Studies in History and Philosophy of Biological and Biomedical Sciences 54:62–71.CrossRefGoogle ScholarPubMed
Suppes, P. 1970. A Probabilistic Theory of Causality. Amsterdam: North-Holland.Google Scholar
Walter, S. D. 1976. “The Estimation and Interpretation of Attributable Risk in Health Research.” Biometrics 32:829–49.CrossRefGoogle ScholarPubMed
Worrall, John. 2010. “Do We Need Some Large, Simple Randomized Trials in Medicine?” In EPSA Philosophical Issues in the Sciences, ed. Suarez, Mauricio, Dorato, Mauro, and Redei, Miklos. Dordrecht: Springer.Google Scholar