Variability in predictions from online tools: A demonstration using internet-based melanoma predictors Journal Article

Authors: Zabor, E. C.; Coit, D.; Gershenwald, J. E.; McMasters, K. M.; Michaelson, J. S.; Stromberg, A. J.; Panageas, K. S.
Article Title: Variability in predictions from online tools: A demonstration using internet-based melanoma predictors
Abstract: Background: Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease. Methods: To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index. Results: In this demonstration project, we found important differences across the three models that led to variability in individual patients’ predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models. Conclusions: This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool’s limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians. © 2018, Society of Surgical Oncology.
Keywords: adult; cancer survival; controlled study; aged; major clinical study; cancer patient; melanoma; internet; survival prediction; cancer prognosis; human; male; female; article
Journal Title: Annals of Surgical Oncology
Volume: 25
Issue: 8
ISSN: 1068-9265
Publisher: Springer  
Date Published: 2018-08-01
Start Page: 2172
End Page: 2177
Language: English
DOI: 10.1245/s10434-018-6370-4
PROVIDER: scopus
PUBMED: 29470818
Notes: Article -- Export Date: 1 August 2018 -- Source: Scopus
Altmetric Score
MSK Authors
  1. Emily Craig Zabor
    131 Zabor
  2. Katherine S Panageas
    329 Panageas
  3. Daniel Coit
    420 Coit