We used bootstrapping to evaluate the predictive performance of both trees on discrimination using the Area under the Receiver-Operating-Characteristic curve, (AUROC) curve, and on prediction accuracy by means of the Brier score (i.e., the mean squared error of the prediction). We also inspected calibration with calibration curves. Internal model validation of the AUROC and Brier measures was based on 200 bootstrap samples and 95% confidence intervals were computed from the resampling distribution with percentile intervals. Calibration curves were based on 10-fold cross validation.
We did post hoc validation analyses in which we tested both trees on a dataset including patients up to January 1st, 2023.
Copyright and License information: The Author(s) ©2023 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Do you have any questions about this protocol?
Post your question to gather feedback from the community. We will also invite the authors of this
article to respond.
Ask a
question
Post a Question
