CTIVA: Censored time interval variable analysis.
Traditionally, datasets with multiple censored time-to-events have not been utilized in multivariate analysis because of their high level of complexity. In this paper, we propose the Censored Time Interval Analysis (CTIVA) method to address this issue. It estimates the joint probability distribution...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294513&type=printable |
| _version_ | 1797394567806320640 |
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| author | Insoo Kim Junhee Seok Yoojoong Kim |
| author_facet | Insoo Kim Junhee Seok Yoojoong Kim |
| author_sort | Insoo Kim |
| collection | DOAJ |
| description | Traditionally, datasets with multiple censored time-to-events have not been utilized in multivariate analysis because of their high level of complexity. In this paper, we propose the Censored Time Interval Analysis (CTIVA) method to address this issue. It estimates the joint probability distribution of actual event times in the censored dataset by implementing a statistical probability density estimation technique on the dataset. Based on the acquired event time, CTIVA investigates variables correlated with the interval time of events via statistical tests. The proposed method handles both categorical and continuous variables simultaneously-thus, it is suitable for application on real-world censored time-to-event datasets, which include both categorical and continuous variables. CTIVA outperforms traditional censored time-to-event data handling methods by 5% on simulation data. The average area under the curve (AUC) of the proposed method on the simulation dataset exceeds 0.9 under various conditions. Further, CTIVA yields novel results on National Sample Cohort Demo (NSCD) and proteasome inhibitor bortezomib dataset, a real-world censored time-to-event dataset of medical history of beneficiaries provided by the National Health Insurance Sharing Service (NHISS) and National Center for Biotechnology Information (NCBI). We believe that the development of CTIVA is a milestone in the investigation of variables correlated with interval time of events in presence of censoring. |
| first_indexed | 2024-03-09T00:21:39Z |
| format | Article |
| id | doaj.art-bf85367be1384332a7bf2c5551ac8481 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-09T00:21:39Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-bf85367be1384332a7bf2c5551ac84812023-12-12T05:34:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-011811e029451310.1371/journal.pone.0294513CTIVA: Censored time interval variable analysis.Insoo KimJunhee SeokYoojoong KimTraditionally, datasets with multiple censored time-to-events have not been utilized in multivariate analysis because of their high level of complexity. In this paper, we propose the Censored Time Interval Analysis (CTIVA) method to address this issue. It estimates the joint probability distribution of actual event times in the censored dataset by implementing a statistical probability density estimation technique on the dataset. Based on the acquired event time, CTIVA investigates variables correlated with the interval time of events via statistical tests. The proposed method handles both categorical and continuous variables simultaneously-thus, it is suitable for application on real-world censored time-to-event datasets, which include both categorical and continuous variables. CTIVA outperforms traditional censored time-to-event data handling methods by 5% on simulation data. The average area under the curve (AUC) of the proposed method on the simulation dataset exceeds 0.9 under various conditions. Further, CTIVA yields novel results on National Sample Cohort Demo (NSCD) and proteasome inhibitor bortezomib dataset, a real-world censored time-to-event dataset of medical history of beneficiaries provided by the National Health Insurance Sharing Service (NHISS) and National Center for Biotechnology Information (NCBI). We believe that the development of CTIVA is a milestone in the investigation of variables correlated with interval time of events in presence of censoring.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294513&type=printable |
| spellingShingle | Insoo Kim Junhee Seok Yoojoong Kim CTIVA: Censored time interval variable analysis. PLoS ONE |
| title | CTIVA: Censored time interval variable analysis. |
| title_full | CTIVA: Censored time interval variable analysis. |
| title_fullStr | CTIVA: Censored time interval variable analysis. |
| title_full_unstemmed | CTIVA: Censored time interval variable analysis. |
| title_short | CTIVA: Censored time interval variable analysis. |
| title_sort | ctiva censored time interval variable analysis |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294513&type=printable |
| work_keys_str_mv | AT insookim ctivacensoredtimeintervalvariableanalysis AT junheeseok ctivacensoredtimeintervalvariableanalysis AT yoojoongkim ctivacensoredtimeintervalvariableanalysis |