J Dig Cancer Res.
2023 Dec;11(3):157-164. 10.52927/jdcr.2023.11.3.157.
Development and Validation of a Prediction Model: Application to Digestive Cancer Research
- Affiliations
-
- 1Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea
- 2Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- KMID: 2549667
- DOI: http://doi.org/10.52927/jdcr.2023.11.3.157
Abstract
- Prediction is a significant topic in clinical research. The development and validation of a prediction model has been increasingly published in clinical research. In this review, we investigated analytical methods and validation schemes for a clinical prediction model used in digestive cancer research. Deep learning and logistic regression, with split-sample validation as an internal or external validation, were the most commonly used methods. Furthermore, we briefly introduced and summarized the advantages and disadvantages of each method. Finally, we discussed several points to consider when conducting prediction model studies.
- Full Text Links
-
- Actions
-
Cited
- CITED
-
- Close
- Share
-
- Similar articles
-
- How to Establish Clinical Prediction Models
- Machine Learning vs. Statistical Model for Prediction Modelling: Application in Medical Imaging Research
- Comparison of survival prediction models for pancreatic cancer: Cox model versus machine learning models
- Nomogram Development and External Validation for Predicting the Risk of Lymph Node Metastasis in T1 Colorectal Cancer
- Development and validation of a 1-year postnephrectomy estimated glomerular filtration rate prediction model using preoperative factors
