Academic Journal
Non-Markov multi-state survival analysis with complex censoring: A structured synthesis of models, estimators, and applications.
| Title: | Non-Markov multi-state survival analysis with complex censoring: A structured synthesis of models, estimators, and applications. |
|---|---|
| Authors: | AZEVEDO, MARTA, MEIRA-MACHADO, LUÍS, MOREIRA, CARLA |
| Source: | Chilean Journal of Statistics (ChJS); Dec2025, Vol. 16 Issue 2, p125-176, 52p |
| Subject Terms: | Clinical trials, Biomarkers, Control groups, Bayesian analysis, Time-varying systems, Event history analysis, Research methodology |
| Abstract: | Reliable quantification of treatment benefit in late-phase clinical trials increasingly requires modeling patient histories that include progression, adverse events, and treatment 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 meta-data. Addressing these priorities may enhance both the methodological robustness and regulatory applicability of non-Markov survival models. [ABSTRACT FROM AUTHOR] |
| Copyright of Chilean Journal of Statistics (ChJS) is the property of Sociedad Chilena de Estadistica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 190871956 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1040.81262207031 |
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| Items | – Name: Title Label: Title Group: Ti Data: Non-Markov multi-state survival analysis with complex censoring: A structured synthesis of models, estimators, and applications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22AZEVEDO%2C+MARTA%22">AZEVEDO, MARTA</searchLink><br /><searchLink fieldCode="AR" term="%22MEIRA-MACHADO%2C+LUÍS%22">MEIRA-MACHADO, LUÍS</searchLink><br /><searchLink fieldCode="AR" term="%22MOREIRA%2C+CARLA%22">MOREIRA, CARLA</searchLink> – Name: TitleSource Label: Source Group: Src Data: Chilean Journal of Statistics (ChJS); Dec2025, Vol. 16 Issue 2, p125-176, 52p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Clinical+trials%22">Clinical trials</searchLink><br /><searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink><br /><searchLink fieldCode="DE" term="%22Control+groups%22">Control groups</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Time-varying+systems%22">Time-varying systems</searchLink><br /><searchLink fieldCode="DE" term="%22Event+history+analysis%22">Event history analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Reliable quantification of treatment benefit in late-phase clinical trials increasingly requires modeling patient histories that include progression, adverse events, and treatment 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 meta-data. Addressing these priorities may enhance both the methodological robustness and regulatory applicability of non-Markov survival models. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Chilean Journal of Statistics (ChJS) is the property of Sociedad Chilena de Estadistica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=190871956 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.32372/ChJS.16-02-02 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 52 StartPage: 125 Subjects: – SubjectFull: Clinical trials Type: general – SubjectFull: Biomarkers Type: general – SubjectFull: Control groups Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Time-varying systems Type: general – SubjectFull: Event history analysis Type: general – SubjectFull: Research methodology Type: general Titles: – TitleFull: Non-Markov multi-state survival analysis with complex censoring: A structured synthesis of models, estimators, and applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: AZEVEDO, MARTA – PersonEntity: Name: NameFull: MEIRA-MACHADO, LUÍS – PersonEntity: Name: NameFull: MOREIRA, CARLA IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 07187912 Numbering: – Type: volume Value: 16 – Type: issue Value: 2 Titles: – TitleFull: Chilean Journal of Statistics (ChJS) Type: main |
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