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What's Time Got to Do with It? Inattention to Duration in Interpretation of Survival Graphs

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  • Brian J. Zikmund‐Fisher
  • Angela Fagerlin
  • Peter A. Ubel

Abstract

Reports of randomized clinical trials often use survival curves to summarize clinical outcomes over time and graphically demonstrate evidence of treatment effectiveness. Survival curves can also be used in patient communications to display how health risks accumulate over time. In a randomized survey experiment, administered online, we tested whether people viewing survival curves appropriately adjust their risk perceptions to account for the duration shown. Internet users (N= 864) were recruited from a demographically balanced U.S. panel. Participants read about a hypothetical disease and then viewed one of four survival graphs that displayed mortality risks with and without treatment. Survival graphs showed either a visually large or visually small difference between treatments and were labeled to represent either 5‐year or 15‐year risk statistics. Participants then provided ratings of disease seriousness, as well as treatment effectiveness for each possible treatment. Variations in ratings corresponded more with visual dissimilarity than with changes in the statistical risk exhibited, with participants perceiving somewhat greater disease seriousness and significant differences in treatment effectiveness in large visual difference graphs. We conclude that when people interpret survival curves, they often fail to sufficiently account for the timeframe represented and perceive more risk and larger differences when identical risks are displayed over longer periods of time. We recommend that all presentations of survival graphics, whether to patients, physicians, or scientists, emphasize duration information (e.g., in the title) and remind readers that attending to graph axis labels is the only way to pierce these visual illusions.

Suggested Citation

  • Brian J. Zikmund‐Fisher & Angela Fagerlin & Peter A. Ubel, 2005. "What's Time Got to Do with It? Inattention to Duration in Interpretation of Survival Graphs," Risk Analysis, John Wiley & Sons, vol. 25(3), pages 589-595, June.
    • Handle: RePEc:wly:riskan:v:25:y:2005:i:3:p:589-595
    DOI: 10.1111/j.1539-6924.2005.00626.x
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    References listed on IDEAS

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    1. Dennis J. Mazur & David H. Hickam, 1993. "Patients' and Physicians' Interpretations of Graphic Data Displays," Medical Decision Making, , vol. 13(1), pages 59-63, February.
    2. Katrina Armstrong & J. Sanford Schwartz & Genevieve Fitzgerald & Mary Putt & Peter A. Ubel, 2002. "Effect of Framing as Gain versus Loss on Understanding and Hypothetical Treatment Choices: Survival and Mortality Curves," Medical Decision Making, , vol. 22(1), pages 76-83, February.
    3. Ernest W. Lau & G. A. Ng, 2002. "Visual Illusions Created by Survival Curves and the Need to Avoid Potential Misinterpretation," Medical Decision Making, , vol. 22(3), pages 238-244, June.
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    Cited by:

    1. Walton Sumner & Eric Ding & Irene D. Fischer & Michael D. Hagen, 2014. "Methods for Performing Survival Curve Quality-of-Life Assessments," Medical Decision Making, , vol. 34(6), pages 787-799, August.
    2. Daniel A. Hamstra & Skyler B. Johnson & Stephanie Daignault & Brian J. Zikmund-Fisher & Jeremy M. G. Taylor & Knoll Larkin & Alexander Wood & Angela Fagerlin, 2015. "The Impact of Numeracy on Verbatim Knowledge of the Longitudinal Risk for Prostate Cancer Recurrence following Radiation Therapy," Medical Decision Making, , vol. 35(1), pages 27-36, January.
    3. Carmen Keller & Michael Siegrist & Heinz Gutscher, 2006. "The Role of the Affect and Availability Heuristics in Risk Communication," Risk Analysis, John Wiley & Sons, vol. 26(3), pages 631-639, June.

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