Journal of clinical epidemiology · 2024
Implementation of a dynamic model updating pipeline provides a systematic process for maintaining performance of prediction models.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Tanner Kamaryn T, Diaz-Ordaz Karla, Keogh Ruth H
Аннотация
OBJECTIVES: We describe the steps for implementing a dynamic updating pipeline for clinical prediction models and illustrate the proposed methods in an application of 5-year survival prediction in cystic fibrosis.
STUDY DESIGN AND SETTING: Dynamic model updating refers to the process of repeated updating of a clinical prediction model with new information to counter performance degradation. We describe 2 types of updating pipeline: "proactive updating" where candidate model updates are tested any time new data are available, and "reactive updating" where updates are only made when performance of the current model declines or the model structure changes. Methods for selecting the best candidate updating model are based on measures of predictive performance under the 2 pipelines. The methods are illustrated in our motivating example of a 5-year survival prediction model in cystic fibrosis. Over a dynamic updating period of 10 years, we report the updating decisions made and the performance of the prediction models selected under each pipeline.
RESULTS: Both the proactive and reactive updating pipelines produced survival prediction models that overall had better performance in terms of calibration and discrimination than a model that was not updated. Further, use of the dynamic updating pipelines ensured that the prediction model's performance was consistently and frequently reviewed in new data.
CONCLUSION: Implementing a dynamic updating pipeline will help guard against model performance degradation while ensuring that the updating process is principled and data-driven.
STUDY DESIGN AND SETTING: Dynamic model updating refers to the process of repeated updating of a clinical prediction model with new information to counter performance degradation. We describe 2 types of updating pipeline: "proactive updating" where candidate model updates are tested any time new data are available, and "reactive updating" where updates are only made when performance of the current model declines or the model structure changes. Methods for selecting the best candidate updating model are based on measures of predictive performance under the 2 pipelines. The methods are illustrated in our motivating example of a 5-year survival prediction model in cystic fibrosis. Over a dynamic updating period of 10 years, we report the updating decisions made and the performance of the prediction models selected under each pipeline.
RESULTS: Both the proactive and reactive updating pipelines produced survival prediction models that overall had better performance in terms of calibration and discrimination than a model that was not updated. Further, use of the dynamic updating pipelines ensured that the prediction model's performance was consistently and frequently reviewed in new data.
CONCLUSION: Implementing a dynamic updating pipeline will help guard against model performance degradation while ensuring that the updating process is principled and data-driven.