Publicação
Non-Markov multi-state survival analysis with complex censoring: a structured synthesis of models, estimators, and applications
| Resumo: | Reliable quantification of treatment benefit in late-phase clinical trials increasingly requires modeling patient histories that include progression, adverse events, and treat ment switches. Conventional multi-state analyses often invoke the Markov property and assume independent right censoring— conditions rarely satisfied in oncology, immunology, or cell-therapy programs, where intermediate events and informative dropout are common. This article presents a systematic review and bibliometric synthesis of 48 peer reviewed studies published through 11 June 2025 that (i) relax the Markov assumption and (ii) address complex observation schemes such as left truncation, interval censoring, or informative censoring, identified through Web of Science and Scopus searches following preferred reporting items for systematic reviews and meta-analyses 2020 guidelines. A recurring set of methodological strategies emerges across the literature, including semi-Markov transition-intensity models, illness–death and semi-competing risks frameworks, landmarking for dynamic prediction, and inverse-probability-of-censoring weighting. Estimation approaches range from nonparametric product integrals to semiparametric weighted likelihoods and Bayesian Markov chain Monte Carlo, with recent contributions exploring saddle-point approximations and subsampling for large-scale electronic health records. To complement this synthesis, we include a compact simulation contrasting baseline and landmark Aalen–Johansen estimators under semi-Markov dynamics with history-dependent censoring, and a bibliometric network analysis mapping collaboration patterns, thematic clusters, and structural gaps. The findings highlight the need for scalable, auditable software, robust diagnostics aligned with the International Council for Harmonization E9(R1) estimand framework (which links clinical trial objectives to precise statistical targets), and better integration of high-dimensional biomarkers; limitations include the English-language restriction and reliance on bibliometric metadata. Addressing these priorities may enhance both the methodological robustness and regulatory applicability of non-Markov survival models. |
|---|---|
| Autores principais: | Azevedo, Marta Vasconcelos Castro |
| Outros Autores: | Machado, Luís Meira; Moreira, Carla Maria Gonçalves Macedo |
| Assunto: | History-dependent censoring Interval/panel observation Left truncation Non-Markov inference PRISMA Pseudo-observations |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1866878289327423488 |
|---|---|
| author | Azevedo, Marta Vasconcelos Castro |
| author2 | Machado, Luís Meira Moreira, Carla Maria Gonçalves Macedo |
| author2_role | author author |
| author_facet | Azevedo, Marta Vasconcelos Castro Machado, Luís Meira Moreira, Carla Maria Gonçalves Macedo |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Azevedo, Marta Vasconcelos Castro\"},{\"Person.name\":\"Machado, Luís Meira\"},{\"Person.name\":\"Moreira, Carla Maria Gonçalves Macedo\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Azevedo, Marta Vasconcelos Castro Machado, Luís Meira Moreira, Carla Maria Gonçalves Macedo |
| datacite.date.Accepted.fl_str_mv | 2025-01-01T00:00:00Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | History-dependent censoring Interval/panel observation Left truncation Non-Markov inference PRISMA Pseudo-observations |
| datacite.titles.title.fl_str_mv | Non-Markov multi-state survival analysis with complex censoring: a structured synthesis of models, estimators, and applications |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | Azevedo, Marta Vasconcelos Castro Machado, Luís Meira Moreira, Carla Maria Gonçalves Macedo |
| dc.date.Accepted.fl_str_mv | 2025-01-01T00:00:00Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/99211 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Chilean Statistical Society |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by-nc-sa/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 | History-dependent censoring Interval/panel observation Left truncation Non-Markov inference PRISMA Pseudo-observations |
| dc.title.fl_str_mv | Non-Markov multi-state survival analysis with complex censoring: a structured synthesis of models, estimators, and applications |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Reliable quantification of treatment benefit in late-phase clinical trials increasingly requires modeling patient histories that include progression, adverse events, and treat ment switches. Conventional multi-state analyses often invoke the Markov property and assume independent right censoring— conditions rarely satisfied in oncology, immunology, or cell-therapy programs, where intermediate events and informative dropout are common. This article presents a systematic review and bibliometric synthesis of 48 peer reviewed studies published through 11 June 2025 that (i) relax the Markov assumption and (ii) address complex observation schemes such as left truncation, interval censoring, or informative censoring, identified through Web of Science and Scopus searches following preferred reporting items for systematic reviews and meta-analyses 2020 guidelines. A recurring set of methodological strategies emerges across the literature, including semi-Markov transition-intensity models, illness–death and semi-competing risks frameworks, landmarking for dynamic prediction, and inverse-probability-of-censoring weighting. Estimation approaches range from nonparametric product integrals to semiparametric weighted likelihoods and Bayesian Markov chain Monte Carlo, with recent contributions exploring saddle-point approximations and subsampling for large-scale electronic health records. To complement this synthesis, we include a compact simulation contrasting baseline and landmark Aalen–Johansen estimators under semi-Markov dynamics with history-dependent censoring, and a bibliometric network analysis mapping collaboration patterns, thematic clusters, and structural gaps. The findings highlight the need for scalable, auditable software, robust diagnostics aligned with the International Council for Harmonization E9(R1) estimand framework (which links clinical trial objectives to precise statistical targets), and better integration of high-dimensional biomarkers; limitations include the English-language restriction and reliance on bibliometric metadata. Addressing these priorities may enhance both the methodological robustness and regulatory applicability of non-Markov survival models. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorium.uminho.pt/bitstreams/20c9dd07-6a0b-4ba1-89c0-e0f5da755739/download |
| funding.funder.alternateName_str_mv | other other |
| funding.funder.identifier_str_mv | urn:openaire:fct_________::FCT urn:openaire:fct_________::FCT |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia, I.P. Fundação para a Ciência e a Tecnologia, I.P. |
| funding.identifier_str_mv | UID/00013/2025 2023.14897.PEX |
| funding.name_str_mv | Avaliação UID 2023/2024 Concurso de Projetos Exploratórios em Todos os Domínios Científicos 2023 |
| funding_str_mv | UID/00013/2025 https://hdl.handle.net/1822/98696 2023.14897.PEX https://hdl.handle.net/1822/99238 |
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| identifier.url.fl_str_mv | https://hdl.handle.net/1822/99211 |
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| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
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| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/99211 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Azevedo, Marta Vasconcelos Castro Machado, Luís Meira Moreira, Carla Maria Gonçalves Macedo |
| publishDate | 2025 |
| publisher.none.fl_str_mv | Chilean Statistical Society |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
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| spelling | engChilean Statistical SocietyengReliable quantification of treatment benefit in late-phase clinical trials increasingly requires modeling patient histories that include progression, adverse events, and treat ment switches. Conventional multi-state analyses often invoke the Markov property and assume independent right censoring— conditions rarely satisfied in oncology, immunology, or cell-therapy programs, where intermediate events and informative dropout are common. This article presents a systematic review and bibliometric synthesis of 48 peer reviewed studies published through 11 June 2025 that (i) relax the Markov assumption and (ii) address complex observation schemes such as left truncation, interval censoring, or informative censoring, identified through Web of Science and Scopus searches following preferred reporting items for systematic reviews and meta-analyses 2020 guidelines. A recurring set of methodological strategies emerges across the literature, including semi-Markov transition-intensity models, illness–death and semi-competing risks frameworks, landmarking for dynamic prediction, and inverse-probability-of-censoring weighting. Estimation approaches range from nonparametric product integrals to semiparametric weighted likelihoods and Bayesian Markov chain Monte Carlo, with recent contributions exploring saddle-point approximations and subsampling for large-scale electronic health records. To complement this synthesis, we include a compact simulation contrasting baseline and landmark Aalen–Johansen estimators under semi-Markov dynamics with history-dependent censoring, and a bibliometric network analysis mapping collaboration patterns, thematic clusters, and structural gaps. The findings highlight the need for scalable, auditable software, robust diagnostics aligned with the International Council for Harmonization E9(R1) estimand framework (which links clinical trial objectives to precise statistical targets), and better integration of high-dimensional biomarkers; limitations include the English-language restriction and reliance on bibliometric metadata. Addressing these priorities may enhance both the methodological robustness and regulatory applicability of non-Markov survival models.application/pdfengNon-Markov multi-state survival analysis with complex censoring: a structured synthesis of models, estimators, and applicationsAzevedo, Marta Vasconcelos CastroMachado, Luís MeiraMoreira, Carla Maria Gonçalves MacedoHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISSNIsPartOf0718-7912DOIIsPartOf10.32372/ChJS.16-02-0220252025-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/99211http://purl.org/coar/access_right/c_abf2open accessHistory-dependent censoringInterval/panel observationLeft truncationNon-Markov inferencePRISMAPseudo-observations1313176 bytesFundação para a Ciência e a Tecnologia, I.P.Center of Mathematics of the University of Minho (UID/00013/2025)Avaliação UID 2023/2024https://hdl.handle.net/1822/98696UID/00013/2025Crossref Funder IDurn:openaire:fct_________::FCTFundação para a Ciência e a Tecnologia, I.P.Complex Time-to-Event Analysis: Multistate Models and Cohort-Based Studies (2023.14897.PEX)Concurso de Projetos Exploratórios em Todos os Domínios Científicos 2023https://hdl.handle.net/1822/992382023.14897.PEXCrossref Funder IDurn:openaire:fct_________::FCTliteraturehttp://purl.org/coar/resource_type/c_6501journal article2025http://creativecommons.org/licenses/by-nc-sa/4.0/openAccesshttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorium.uminho.pt/bitstreams/20c9dd07-6a0b-4ba1-89c0-e0f5da755739/download162125177 |
| spellingShingle | Non-Markov multi-state survival analysis with complex censoring: a structured synthesis of models, estimators, and applications Azevedo, Marta Vasconcelos Castro History-dependent censoring Interval/panel observation Left truncation Non-Markov inference PRISMA Pseudo-observations |
| status | SINGLETON |
| subject.fl_str_mv | History-dependent censoring Interval/panel observation Left truncation Non-Markov inference PRISMA Pseudo-observations |
| title | Non-Markov multi-state survival analysis with complex censoring: a structured synthesis of models, estimators, and applications |
| title_full | Non-Markov multi-state survival analysis with complex censoring: a structured synthesis of models, estimators, and applications |
| title_fullStr | Non-Markov multi-state survival analysis with complex censoring: a structured synthesis of models, estimators, and applications |
| title_full_unstemmed | Non-Markov multi-state survival analysis with complex censoring: a structured synthesis of models, estimators, and applications |
| title_short | Non-Markov multi-state survival analysis with complex censoring: a structured synthesis of models, estimators, and applications |
| title_sort | Non-Markov multi-state survival analysis with complex censoring: a structured synthesis of models, estimators, and applications |
| topic | History-dependent censoring Interval/panel observation Left truncation Non-Markov inference PRISMA Pseudo-observations |
| topic_facet | History-dependent censoring Interval/panel observation Left truncation Non-Markov inference PRISMA Pseudo-observations |
| url | https://hdl.handle.net/1822/99211 |
| visible | 1 |
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