Publicação
Parametric landmark estimation of the transition probabilities in survival data with multiple events
| Resumo: | Multi-state models are a useful tool for analyzing survival data with multiple events. The transition probabilities play an important role in these models since they allow for long-term predictions of the process in a simple and summarized manner. Recent papers have used the idea of subsampling to estimate these quantities, providing estimators with superior performance in the case of strong violations of the Markov condition. Subsampling, also referred to as landmarking, leads to small sample sizes and usually heavily censored data, which leads to estimators with higher variability. Here, we use the flexibility of the generalized gamma distribution combined with the same idea of subsampling to obtain estimators free of the Markov condition with less variability. Simulation studies show the good small sample properties of the proposed estimators. The proposed methods are illustrated using real data. |
|---|---|
| Autores principais: | Soutinho, Gustavo |
| Outros Autores: | Machado, Luís Meira |
| Assunto: | Multi-state models parametric estimation transition probabilities Generalized gamma distribution Landmark approach |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1866878051102490624 |
|---|---|
| author | Soutinho, Gustavo |
| author2 | Machado, Luís Meira |
| author2_role | author |
| author_facet | Soutinho, Gustavo Machado, Luís Meira |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Soutinho, Gustavo\"},{\"Person.name\":\"Machado, Luís Meira\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Soutinho, Gustavo Machado, Luís Meira |
| datacite.date.Accepted.fl_str_mv | 2022-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2022-08-02T16:33:48Z |
| datacite.date.embargoed.fl_str_mv | 2022-08-02T16:33:48Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Multi-state models parametric estimation transition probabilities Generalized gamma distribution Landmark approach |
| datacite.titles.title.fl_str_mv | Parametric landmark estimation of the transition probabilities in survival data with multiple events |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | Soutinho, Gustavo Machado, Luís Meira |
| dc.date.Accepted.fl_str_mv | 2022-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2022-08-02T16:33:48Z |
| dc.date.embargoed.fl_str_mv | 2022-08-02T16:33:48Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/79141 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | World Scientific and Engineering Academy and Society (WSEAS) |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.rights.rights.copyright.fl_str_mv | openAccess |
| dc.subject.none.fl_str_mv | Multi-state models parametric estimation transition probabilities Generalized gamma distribution Landmark approach |
| dc.title.fl_str_mv | Parametric landmark estimation of the transition probabilities in survival data with multiple events |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Multi-state models are a useful tool for analyzing survival data with multiple events. The transition probabilities play an important role in these models since they allow for long-term predictions of the process in a simple and summarized manner. Recent papers have used the idea of subsampling to estimate these quantities, providing estimators with superior performance in the case of strong violations of the Markov condition. Subsampling, also referred to as landmarking, leads to small sample sizes and usually heavily censored data, which leads to estimators with higher variability. Here, we use the flexibility of the generalized gamma distribution combined with the same idea of subsampling to obtain estimators free of the Markov condition with less variability. Simulation studies show the good small sample properties of the proposed estimators. The proposed methods are illustrated using real data. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://prod-dspace.uminho.pt/bitstreams/44b72b25-9a38-49ce-87b9-6d4420d34640/download |
| id | rum_f0b3494dbf2dde4bbcc66dcfeea385b2 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/79141 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/79141 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Soutinho, Gustavo Machado, Luís Meira |
| publishDate | 2022 |
| publisher.none.fl_str_mv | World Scientific and Engineering Academy and Society (WSEAS) |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engWorld Scientific and Engineering Academy and Society (WSEAS)porMulti-state models are a useful tool for analyzing survival data with multiple events. The transition probabilities play an important role in these models since they allow for long-term predictions of the process in a simple and summarized manner. Recent papers have used the idea of subsampling to estimate these quantities, providing estimators with superior performance in the case of strong violations of the Markov condition. Subsampling, also referred to as landmarking, leads to small sample sizes and usually heavily censored data, which leads to estimators with higher variability. Here, we use the flexibility of the generalized gamma distribution combined with the same idea of subsampling to obtain estimators free of the Markov condition with less variability. Simulation studies show the good small sample properties of the proposed estimators. The proposed methods are illustrated using real data.application/pdfporParametric landmark estimation of the transition probabilities in survival data with multiple eventsSoutinho, GustavoMachado, Luís MeiraHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISSNIsPartOf1109-2769DOIIsPartOf10.37394/23206.2022.21.272022-08-02T16:33:48Z20222022-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/79141http://purl.org/coar/access_right/c_abf2open accessMulti-state modelsparametric estimationtransition probabilitiesGeneralized gamma distributionLandmark approach623422 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal article2022http://creativecommons.org/licenses/by/4.0/openAccesshttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/44b72b25-9a38-49ce-87b9-6d4420d34640/download |
| spellingShingle | Parametric landmark estimation of the transition probabilities in survival data with multiple events Soutinho, Gustavo Multi-state models parametric estimation transition probabilities Generalized gamma distribution Landmark approach |
| status | SINGLETON |
| subject.fl_str_mv | Multi-state models parametric estimation transition probabilities Generalized gamma distribution Landmark approach |
| title | Parametric landmark estimation of the transition probabilities in survival data with multiple events |
| title_full | Parametric landmark estimation of the transition probabilities in survival data with multiple events |
| title_fullStr | Parametric landmark estimation of the transition probabilities in survival data with multiple events |
| title_full_unstemmed | Parametric landmark estimation of the transition probabilities in survival data with multiple events |
| title_short | Parametric landmark estimation of the transition probabilities in survival data with multiple events |
| title_sort | Parametric landmark estimation of the transition probabilities in survival data with multiple events |
| topic | Multi-state models parametric estimation transition probabilities Generalized gamma distribution Landmark approach |
| topic_facet | Multi-state models parametric estimation transition probabilities Generalized gamma distribution Landmark approach |
| url | https://hdl.handle.net/1822/79141 |
| visible | 1 |