Dynamic risk prediction of survival in liver cirrhosis: A comparison of landmarking approaches.
Electronic health records (EHR) data provides the researcher and physician with the opportunity to improve risk prediction by employing newer, more sophisticated modeling techniques. Rather than treating the impact of predictor variables on health trajectories as static, we explore the use of time-d...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0306328 |
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| author | Mitchell Paukner Daniela P Ladner Lihui Zhao |
| author_facet | Mitchell Paukner Daniela P Ladner Lihui Zhao |
| author_sort | Mitchell Paukner |
| collection | DOAJ |
| description | Electronic health records (EHR) data provides the researcher and physician with the opportunity to improve risk prediction by employing newer, more sophisticated modeling techniques. Rather than treating the impact of predictor variables on health trajectories as static, we explore the use of time-dependent variables in dynamically modeling time-to-event data through the use of landmarking (LM) data sets. We compare several different dynamic models presented in the literature that utilize LM data sets as the basis of their approach. These techniques include using pseudo-means, pseudo-survival probabilities, and the traditional Cox model. The models are primarily compared with their static counterparts using appropriate measures of model discrimination and calibration based on what summary measure is employed for the response variable. |
| format | Article |
| id | doaj-art-6adb29c3e1c54d028b411b11da08f7ea |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-6adb29c3e1c54d028b411b11da08f7ea2025-08-20T03:17:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01197e030632810.1371/journal.pone.0306328Dynamic risk prediction of survival in liver cirrhosis: A comparison of landmarking approaches.Mitchell PauknerDaniela P LadnerLihui ZhaoElectronic health records (EHR) data provides the researcher and physician with the opportunity to improve risk prediction by employing newer, more sophisticated modeling techniques. Rather than treating the impact of predictor variables on health trajectories as static, we explore the use of time-dependent variables in dynamically modeling time-to-event data through the use of landmarking (LM) data sets. We compare several different dynamic models presented in the literature that utilize LM data sets as the basis of their approach. These techniques include using pseudo-means, pseudo-survival probabilities, and the traditional Cox model. The models are primarily compared with their static counterparts using appropriate measures of model discrimination and calibration based on what summary measure is employed for the response variable.https://doi.org/10.1371/journal.pone.0306328 |
| spellingShingle | Mitchell Paukner Daniela P Ladner Lihui Zhao Dynamic risk prediction of survival in liver cirrhosis: A comparison of landmarking approaches. PLoS ONE |
| title | Dynamic risk prediction of survival in liver cirrhosis: A comparison of landmarking approaches. |
| title_full | Dynamic risk prediction of survival in liver cirrhosis: A comparison of landmarking approaches. |
| title_fullStr | Dynamic risk prediction of survival in liver cirrhosis: A comparison of landmarking approaches. |
| title_full_unstemmed | Dynamic risk prediction of survival in liver cirrhosis: A comparison of landmarking approaches. |
| title_short | Dynamic risk prediction of survival in liver cirrhosis: A comparison of landmarking approaches. |
| title_sort | dynamic risk prediction of survival in liver cirrhosis a comparison of landmarking approaches |
| url | https://doi.org/10.1371/journal.pone.0306328 |
| work_keys_str_mv | AT mitchellpaukner dynamicriskpredictionofsurvivalinlivercirrhosisacomparisonoflandmarkingapproaches AT danielapladner dynamicriskpredictionofsurvivalinlivercirrhosisacomparisonoflandmarkingapproaches AT lihuizhao dynamicriskpredictionofsurvivalinlivercirrhosisacomparisonoflandmarkingapproaches |