구축된 간암 발생 예측 모형의 개선방안에 관한 연구
A study on the updating of prediction model for the development of hepatoma
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Objectives: The statistical prediction models are useful to establishing diagnostic and treatment rule in clinical area. So, there is an increasing interest in building a precise model to predict the probability of diseases for individual patient. In doing that, it is important to reflect the patient's changeable characteristics for improvement of predictive power. In this paper, we studied the methods for the updating of prediction model that add the information of new patients to the existing model. Methods: To update the prediction model, we used an established model including 7 risk factors such as diagnostic type, hepatitic virus type, age, sex, α-FP, ALT, and drinking history and did the re-calibration and shrinkage of intercept and slope of existing one. Results: we considered 4 updating methods, that is, the first one is to use existing model as it is and the second one is to re-calibrate the overall intercept. Also the third one is to re-calibrate overall intercept and slope and the last one is to re-calibrate and shrink overall intercept, and individual slope. Conclusions: Updating methods contain old and new informations. And the model updating method by using many data can be improved predictive power. Especially, the last updating method was found to be the most accurate and useful one.
Abstract
Objectives: The statistical prediction models are useful to establishing diagnostic and treatment rule in clinical area. So, there is an increasing interest in building a precise model to predict the probability of diseases for individual patient. In doing that, it is important to reflect the patient's changeable characteristics for improvement of predictive power. In this paper, we studied the methods for the updating of prediction model that add the information of new patients to the existing model. Methods: To update the prediction model, we used an established model including 7 risk factors such as diagnostic type, hepatitic virus type, age, sex, α-FP, ALT, and drinking history and did the re-calibration and shrinkage of intercept and slope of existing one. Results: we considered 4 updating methods, that is, the first one is to use existing model as it is and the second one is to re-calibrate the overall intercept. Also the third one is to re-calibrate overall intercept and slope and the last one is to re-calibrate and shrink overall intercept, and individual slope. Conclusions: Updating methods contain old and new informations. And the model updating method by using many data can be improved predictive power. Especially, the last updating method was found to be the most accurate and useful one.
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