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[Methodology and progress in adjusting time-dependent covariates in clinical prediction models]
1Key Laboratory of Epidemiology of Major Diseases, Ministry of Education/Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi
|September 4, 2023
Summary
Handling time-dependent covariates in prediction models is crucial for clinical applications. This review explores machine learning, like neural networks, as a powerful alternative to traditional methods for improved model performance.
Area of Science:
- Biostatistics
- Machine Learning
- Predictive Modeling
Background:
- Traditional regression models (landmark, joint models) have limitations in handling time-dependent covariates.
- These limitations restrict the number of predictors and practical scenarios addressable by existing methods.
- Improved methods are needed to enhance prediction model performance and clinical utility.
Conclusions:
- Machine learning offers a more adaptable and powerful approach to handling time-dependent covariates in prediction models.
- This can lead to enhanced model performance and expanded clinical applications.
- Further methodological development is encouraged for optimal utilization of time-dependent covariates.
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