| An empirical study on the two dynamic risk prediction methods based on clinical follow-up data in real-world study |
| Received:September 06, 2021 Revised:October 13, 2021 Click here to download the full text |
| Citation of this paper:YANG Feng,CHEN Xin,YOU Dong-fang,HUANG Li-hong,WEI Zhao-hui,ZHAO Yang.An empirical study on the two dynamic risk prediction methods based on clinical follow-up data in real-world study[J].Chinese Journal of Clinical Medicine,2021,28(5):751-756 |
| Hits: 1385 |
| Download times: 543 |
| Author Name | Affiliation | E-mail | | YANG Feng | School of Public Health School, Nanjing Medical University, Nanjing 211166, Jiangsu, China
Jiaxing Tigermed Data Management Co. Ltd, Jiaxing 314000, Zhejiang, China | | | CHEN Xin | School of Public Health School, Nanjing Medical University, Nanjing 211166, Jiangsu, China
Center of Biomedical Big Data, Nanjing Medical University, Nanjing 210000, Jiangsu, China | | | YOU Dong-fang | School of Public Health School, Nanjing Medical University, Nanjing 211166, Jiangsu, China | | | HUANG Li-hong | Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai 200032, China
CSCO Biostatistics Expert Committee RWE Working Group | | | WEI Zhao-hui | Hangzhou Tigermed Consulting Co. Ltd, Hangzhou 310000, Zhejiang, China
CSCO Biostatistics Expert Committee RWE Working Group | | | ZHAO Yang | School of Public Health School, Nanjing Medical University, Nanjing 211166, Jiangsu, China
Center of Biomedical Big Data, Nanjing Medical University, Nanjing 210000, Jiangsu, China
CSCO Biostatistics Expert Committee RWE Working Group | yzhao@njmu.edu.cn |
|
| Abstract:Objective: To explore the applications of landmarking method and joint modeling for dynamic risk prediction in the analysis of longitudinal dataset obtained from real-world study. Methods: Based on the longitudinal prognosis data of 358 pneumonia patients, we used landmarking method and joint modeling respectively by software R, to estimate the probability of survival for the pneumonia patients who are under observation on day 5, day 10, and day 15. Results: Both of the two methods can make dynamic risk predictions on the probability of outcome in the future at different time points. On day 5, day 10, and day 15, the AUCs of the prediction using landmarking methods were 81.64%, 85.89%, and 82.15%, respectively, while those obtained from the joint modeling were 81.11%, 85.07%, and 72.09%, respectively. Conclusions: In the real-world study for dynamic historical data, dynamic prediction model analysis can be used to obtain more information. |
| keywords:longitudinal data dynamic risk prediction landmark analysis joint modeling |
| HTML View Full Text View/Add Comment Download reader |
|
|
|