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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

334
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
334

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Updated: Jun 17, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Designing the Australian Cancer Atlas: visualizing geostatistical model uncertainty for multiple audiences.

Sarah Goodwin1, Thom Saunders2, Joanne Aitken3

  • 1Human-Centred Computing, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia.

Journal of the American Medical Informatics Association : JAMIA
|August 13, 2024
PubMed
Summary
This summary is machine-generated.

The Australian Cancer Atlas (ACA) provides cancer data visualizations to address health disparities. User-centered design ensured effective communication of cancer incidence and survival estimates for diverse audiences.

Keywords:
cancer atlasdesign studygeostatistical modelgeovisualizationuncertainty

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Area of Science:

  • Geographic health informatics
  • Cancer epidemiology
  • Data visualization

Background:

  • Geographical health disparities in cancer incidence and survival exist in Australia.
  • Effective visualization of cancer data is crucial for identifying and addressing these disparities.

Purpose of the Study:

  • To report on a 21-month user-centered design study for the Australian Cancer Atlas (ACA).
  • To develop and visualize cancer data, focusing on communicating estimate uncertainty to multiple audiences.

Main Methods:

  • Scoping study, literature review, and focus groups informed the design.
  • Digital prototyping and Bayesian model development were conducted in parallel.
  • Iterative feedback from workshops, focus groups, and an advisory group guided development.

Main Results:

  • Identified four key target audiences: general public, researchers, health practitioners, and policymakers.
  • Developed and refined uncertainty visualizations, including wave plots, v-plots, and color transparency.
  • Ensured visualizations were effective for communicating complex cancer data to all identified groups.

Conclusions:

  • The Australian Cancer Atlas (ACA) has been highly successful since its 2018 launch, with over 62,000 users globally.
  • The ACA's user-centered approach and effective uncertainty visualization have led to international replication and a second version launch.
  • This paper offers valuable documentation and lessons learned for developing future cancer atlases.