Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/79143
Título: | Methods for checking the Markov condition in multi-state survival data |
Autor(es): | Soutinho, Gustavo Machado, Luís Meira |
Palavras-chave: | censoring markov assumption multi-state models transition probabilities |
Data: | 2022 |
Editora: | Springer |
Revista: | Computational Statistics |
Resumo(s): | The inference in multi-state models is traditionally performed under a Markov assumption that claims that past and future of the process are independent given the present state. This assumption has an important role in the estimation of the transition probabilities. When the multi-state model is Markovian, the Aalen-Johansen estimator gives consistent estimators of the transition probabilities but this is no longer the case when the process is non-Markovian. Usually, this assumption is checked including covariates depending on the history. Since the landmark methods of the transition probabilities are free of the Markov assumption, they can also be used to introduce such tests by measuring their discrepancy to Markovian estimators. In this paper, we introduce tests for the Markov assumption and compare them with the usual approach based on the analysis of covariates depending on history through simulations. The methods are also compared with more recent and competitive approaches. Three real data examples are included for illustration of the proposed methods. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/79143 |
DOI: | 10.1007/s00180-021-01139-7 |
ISSN: | 0943-4062 |
e-ISSN: | 1613-9658 |
Versão da editora: | https://link.springer.com/content/pdf/10.1007/s00180-021-01139-7.pdf |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CMAT - Artigos em revistas com arbitragem / Papers in peer review journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Paper_Methods_for_checking_the_Markov_condition.pdf | 723,83 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons