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Tytuł:
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Network estimation for censored time-to-event data for multiple events based on multivariate survival analysis.
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Autorzy:
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Kim Y; School of Electrical Engineering, Korea University, Seoul, South Korea.
Seok J; School of Electrical Engineering, Korea University, Seoul, South Korea.
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Źródło:
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PloS one [PLoS One] 2020 Oct 01; Vol. 15 (10), pp. e0239760. Date of Electronic Publication: 2020 Oct 01 (Print Publication: 2020).
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Original Publication: San Francisco, CA : Public Library of Science
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MeSH Terms:
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Multivariate Analysis*
Survival Analysis*
Data Interpretation, Statistical ; Humans ; Models, Statistical ; Statistics as Topic ; Time Factors
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References:
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Int J Epidemiol. 2017 Apr 1;46(2):e15. (PMID: 26822938)
Biometrika. 2011 Dec;98(4):807-820. (PMID: 23049130)
BMJ Open. 2017 Sep 24;7(9):e016640. (PMID: 28947447)
Science. 2003 Jul 4;301(5629):102-5. (PMID: 12843395)
J Pediatr Orthop. 2009 Mar;29(2):142-5. (PMID: 19352239)
AMIA Annu Symp Proc. 2012;2012:716-25. (PMID: 23304345)
J Multivar Anal. 2015 Mar 1;135:153-162. (PMID: 25750463)
Arch Intern Med. 2002 Jun 10;162(11):1229-36. (PMID: 12038940)
Biostatistics. 2014 Jan;15(1):182-95. (PMID: 23902636)
Gut. 1999 Sep;45 Suppl 2:II43-7. (PMID: 10457044)
Biostatistics. 2008 Jul;9(3):432-41. (PMID: 18079126)
J Infect Dis. 1996 Sep;174(3):456-62. (PMID: 8769600)
J Am Stat Assoc. 2009 Jun 1;104(486):735-746. (PMID: 19881892)
Pediatrics. 2000 Apr;105(4 Pt 1):738-42. (PMID: 10742313)
Am Rev Respir Dis. 1992 Oct;146(4):866-70. (PMID: 1416412)
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Entry Date(s):
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Date Created: 20201001 Date Completed: 20201130 Latest Revision: 20201130
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Update Code:
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20240105
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PubMed Central ID:
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PMC7529251
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DOI:
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10.1371/journal.pone.0239760
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PMID:
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33002010
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In general survival analysis, multiple studies have considered a single failure time corresponding to the time to the event of interest or to the occurrence of multiple events under the assumption that each event is independent. However, in real-world events, one event may impact others. Essentially, the potential structure of the occurrence of multiple events can be observed in several survival datasets. The interrelations between the times to the occurrences of events are immensely challenging to analyze because of the presence of censoring. Censoring commonly arises in longitudinal studies in which some events are often not observed for some of the subjects within the duration of research. Although this problem presents the obstacle of distortion caused by censoring, the advanced multivariate survival analysis methods that handle multiple events with censoring make it possible to measure a bivariate probability density function for a pair of events. Considering this improvement, this paper proposes a method called censored network estimation to discover partially correlated relationships and construct the corresponding network composed of edges representing non-zero partial correlations on multiple censored events. To demonstrate its superior performance compared to conventional methods, the selecting power for the partially correlated events was evaluated in two types of networks with iterative simulation experiments. Additionally, the correlation structure was investigated on the electronic health records dataset of the times to the first diagnosis for newborn babies in South Korea. The results show significantly improved performance as compared to edge measurement with competitive methods and reliability in terms of the interrelations of real-life diseases.
Competing Interests: The authors have declared that no competing interests exist.
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