Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model
<p dir="ltr">Masked issues can emerge when dealing with competing risk data. Such issues are exemplified by the cause of a particular failure not being directly exhibited for all units to observe but only proven to be a subset of possible causes of failure. For assessing the impact o...
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2022
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| _version_ | 1864513564642902016 |
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| author | Yosra Yousif (14570989) |
| author2 | Faiz Elfaki (13253412) Meftah Hrairi (14570995) Oyelola Adegboye (3148698) |
| author2_role | author author author |
| author_facet | Yosra Yousif (14570989) Faiz Elfaki (13253412) Meftah Hrairi (14570995) Oyelola Adegboye (3148698) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yosra Yousif (14570989) Faiz Elfaki (13253412) Meftah Hrairi (14570995) Oyelola Adegboye (3148698) |
| dc.date.none.fl_str_mv | 2022-08-23T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/math10173045 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Bayesian_Analysis_of_Masked_Competing_Risks_Data_Based_on_Proportional_Subdistribution_Hazards_Model/23138690 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Data management and data science Mathematical sciences Statistics competing risks masked causes of failure subdistribution hazards MCMC Bayesian analysis |
| dc.title.none.fl_str_mv | Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Masked issues can emerge when dealing with competing risk data. Such issues are exemplified by the cause of a particular failure not being directly exhibited for all units to observe but only proven to be a subset of possible causes of failure. For assessing the impact of explanatory variables (covariates) on the cumulative incidence function (CIF), a process of Bayesian analysis is discussed in this paper. The symmetry assumption is not imposed on the masking probabilities and independent Dirichlet priors assigned to them. The Markov Chain Monte Carlo (MCMC) technique is utilized to implement the Bayesian analysis. The effectiveness of the developed model is tested via numerical studies, including simulated and real data sets.</p><h2>Other Information</h2><p dir="ltr">Published in: Mathematics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.3390/math10173045" target="_blank">https://doi.org/10.3390/math10173045</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8516b7f48a026ddb580c4c168c4a09e8 |
| identifier_str_mv | 10.3390/math10173045 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/23138690 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards ModelYosra Yousif (14570989)Faiz Elfaki (13253412)Meftah Hrairi (14570995)Oyelola Adegboye (3148698)Information and computing sciencesData management and data scienceMathematical sciencesStatisticscompeting risksmasked causes of failuresubdistribution hazardsMCMCBayesian analysis<p dir="ltr">Masked issues can emerge when dealing with competing risk data. Such issues are exemplified by the cause of a particular failure not being directly exhibited for all units to observe but only proven to be a subset of possible causes of failure. For assessing the impact of explanatory variables (covariates) on the cumulative incidence function (CIF), a process of Bayesian analysis is discussed in this paper. The symmetry assumption is not imposed on the masking probabilities and independent Dirichlet priors assigned to them. The Markov Chain Monte Carlo (MCMC) technique is utilized to implement the Bayesian analysis. The effectiveness of the developed model is tested via numerical studies, including simulated and real data sets.</p><h2>Other Information</h2><p dir="ltr">Published in: Mathematics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.3390/math10173045" target="_blank">https://doi.org/10.3390/math10173045</a></p>2022-08-23T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/math10173045https://figshare.com/articles/journal_contribution/Bayesian_Analysis_of_Masked_Competing_Risks_Data_Based_on_Proportional_Subdistribution_Hazards_Model/23138690CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/231386902022-08-23T00:00:00Z |
| spellingShingle | Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model Yosra Yousif (14570989) Information and computing sciences Data management and data science Mathematical sciences Statistics competing risks masked causes of failure subdistribution hazards MCMC Bayesian analysis |
| status_str | publishedVersion |
| title | Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model |
| title_full | Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model |
| title_fullStr | Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model |
| title_full_unstemmed | Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model |
| title_short | Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model |
| title_sort | Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model |
| topic | Information and computing sciences Data management and data science Mathematical sciences Statistics competing risks masked causes of failure subdistribution hazards MCMC Bayesian analysis |