IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0253057.html
   My bibliography  Save this article

EA3: A softmax algorithm for evidence appraisal aggregation

Author

Listed:
  • Francesco De Pretis
  • Jürgen Landes

Abstract

Real World Evidence (RWE) and its uses are playing a growing role in medical research and inference. Prominently, the 21st Century Cures Act—approved in 2016 by the US Congress—permits the introduction of RWE for the purpose of risk-benefit assessments of medical interventions. However, appraising the quality of RWE and determining its inferential strength are, more often than not, thorny problems, because evidence production methodologies may suffer from multiple imperfections. The problem arises to aggregate multiple appraised imperfections and perform inference with RWE. In this article, we thus develop an evidence appraisal aggregation algorithm called EA3. Our algorithm employs the softmax function—a generalisation of the logistic function to multiple dimensions—which is popular in several fields: statistics, mathematical physics and artificial intelligence. We prove that EA3 has a number of desirable properties for appraising RWE and we show how the aggregated evidence appraisals computed by EA3 can support causal inferences based on RWE within a Bayesian decision making framework. We also discuss features and limitations of our approach and how to overcome some shortcomings. We conclude with a look ahead at the use of RWE.

Suggested Citation

  • Francesco De Pretis & Jürgen Landes, 2021. "EA3: A softmax algorithm for evidence appraisal aggregation," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-23, June.
  • Handle: RePEc:plo:pone00:0253057
    DOI: 10.1371/journal.pone.0253057
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253057
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0253057&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0253057?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Mohammed T Ansari & Alexander Tsertsvadze & David Moher, 2009. "Grading Quality of Evidence and Strength of Recommendations: A Perspective," PLOS Medicine, Public Library of Science, vol. 6(9), pages 1-3, September.
    2. Gavin B Stewart & Julian P T Higgins & Holger Schünemann & Nick Meader, 2015. "The Use of Bayesian Networks to Assess the Quality of Evidence from Research Synthesis: 1," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-13, April.
    3. Kwon, Yongchan & Won, Joong-Ho & Kim, Beom Joon & Paik, Myunghee Cho, 2020. "Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
    4. Alexis Llewellyn & Craig Whittington & Gavin Stewart & Julian PT Higgins & Nick Meader, 2015. "The Use of Bayesian Networks to Assess the Quality of Evidence from Research Synthesis: 2. Inter-Rater Reliability and Comparison with Standard GRADE Assessment," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chandrashekhar Sreeramareddy & TN Sathyanarayana & Raghupathy Anchala & HN Harsha Kumar, 2015. "PROTOCOL: Family and Community Interventions under Integrated Management of Childhood Illness Strategy for Reduction of Neonatal and Under‐five Mortality among Children in Low‐And‐Middle‐Income Countr," Campbell Systematic Reviews, John Wiley & Sons, vol. 11(1), pages 1-50.
    2. Laura Maxim & Jeroen P van der Sluijs, 2014. "Qualichem In Vivo: A Tool for Assessing the Quality of In Vivo Studies and Its Application for Bisphenol A," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-16, January.
    3. Sun Ho Ro & Jie Gong, 2024. "Scalable approach to create annotated disaster image database supporting AI-driven damage assessment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 11693-11712, October.
    4. Lihuan Guo & Wei Wang & Yenchun Jim Wu, 2023. "What Do Scholars Propose for Future COVID-19 Research in Academic Publications? A Topic Analysis Based on Autoencoder," SAGE Open, , vol. 13(2), pages 21582440231, June.
    5. Zeng, Runtian & Song, Qiankun, 2024. "Mean-square exponential input-to-state stability for stochastic neutral-type quaternion-valued neural networks via Itô’s formula of quaternion version," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    6. Kathryn Oliver & Warren Pearce, 2017. "Three lessons from evidence-based medicine and policy: increase transparency, balance inputs and understand power," Palgrave Communications, Palgrave Macmillan, vol. 3(1), pages 1-7, December.
    7. Alexis Llewellyn & Craig Whittington & Gavin Stewart & Julian PT Higgins & Nick Meader, 2015. "The Use of Bayesian Networks to Assess the Quality of Evidence from Research Synthesis: 2. Inter-Rater Reliability and Comparison with Standard GRADE Assessment," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-11, December.
    8. Matthias Griebel & Dennis Segebarth & Nikolai Stein & Nina Schukraft & Philip Tovote & Robert Blum & Christoph M. Flath, 2023. "Deep learning-enabled segmentation of ambiguous bioimages with deepflash2," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    9. Amritanand Sebastian & Rahul Pendurthi & Azimkhan Kozhakhmetov & Nicholas Trainor & Joshua A. Robinson & Joan M. Redwing & Saptarshi Das, 2022. "Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0253057. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.