Multistate recursively imputed survival trees for time-to-event data analysis: an application to AIDS and mortality post-HIV infection data
Abstracts Background This study aimed to introduce recursively imputed survival trees into multistate survival models (MSRIST) to analyze these types of data and to identify the prognostic factors influencing the disease progression in patients with intermediate events. The proposed method is fully...
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| Language: | English |
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BMC
2018-11-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | http://link.springer.com/article/10.1186/s12874-018-0596-5 |
| _version_ | 1818134915499163648 |
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| author | Leili Tapak Michael R. Kosorok Majid Sadeghifar Omid Hamidi |
| author_facet | Leili Tapak Michael R. Kosorok Majid Sadeghifar Omid Hamidi |
| author_sort | Leili Tapak |
| collection | DOAJ |
| description | Abstracts Background This study aimed to introduce recursively imputed survival trees into multistate survival models (MSRIST) to analyze these types of data and to identify the prognostic factors influencing the disease progression in patients with intermediate events. The proposed method is fully nonparametric and can be used for estimating transition probabilities. Methods A general algorithm was provided for analyzing multi-state data with a focus on the illness-death and progressive multi-state models. The model considered both beyond Markov and Non-Markov settings. We also proposed a multi-state random survival method (MSRSF) and compared their performance with the classical multi-state Cox model. We applied the proposed method to a dataset related to HIV/AIDS patients based on a retrospective cohort study extracted in Tehran from April 2004 to March 2014 consist of 2473 HIV-infected patients. Results The results showed that MSRIST outperformed the classical multistate method using Cox Model and MSRSF in terms of integrated Brier score and concordance index over 500 repetitions. We also identified a set of important risk factors as well as their interactions on different states of HIV and AIDS progression. Conclusions There are different strategies for modelling the intermediate event. We adapted two newly developed data mining technique (RSF and RIST) for multistate models (MSRSF and MSRIST) to identify important risk factors in different stages of the diseases. The methods can capture any complex relationship between variables and can be used as a useful tool for identifying important risk factors in different states of this disease. |
| first_indexed | 2024-12-11T09:16:12Z |
| format | Article |
| id | doaj.art-741088c776e040d4ab8975080d0de394 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-12-11T09:16:12Z |
| publishDate | 2018-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-741088c776e040d4ab8975080d0de3942022-12-22T01:13:22ZengBMCBMC Medical Research Methodology1471-22882018-11-0118111210.1186/s12874-018-0596-5Multistate recursively imputed survival trees for time-to-event data analysis: an application to AIDS and mortality post-HIV infection dataLeili Tapak0Michael R. Kosorok1Majid Sadeghifar2Omid Hamidi3Department of Biostatistics, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical SciencesDepartment of Biostatistics, Department of Statistics and Operations Research, University of North Carolina at Chapel HillDepartment of Statistics, Bu-Ali Sina UniversityDepartment of Science, Hamedan University of TechnologyAbstracts Background This study aimed to introduce recursively imputed survival trees into multistate survival models (MSRIST) to analyze these types of data and to identify the prognostic factors influencing the disease progression in patients with intermediate events. The proposed method is fully nonparametric and can be used for estimating transition probabilities. Methods A general algorithm was provided for analyzing multi-state data with a focus on the illness-death and progressive multi-state models. The model considered both beyond Markov and Non-Markov settings. We also proposed a multi-state random survival method (MSRSF) and compared their performance with the classical multi-state Cox model. We applied the proposed method to a dataset related to HIV/AIDS patients based on a retrospective cohort study extracted in Tehran from April 2004 to March 2014 consist of 2473 HIV-infected patients. Results The results showed that MSRIST outperformed the classical multistate method using Cox Model and MSRSF in terms of integrated Brier score and concordance index over 500 repetitions. We also identified a set of important risk factors as well as their interactions on different states of HIV and AIDS progression. Conclusions There are different strategies for modelling the intermediate event. We adapted two newly developed data mining technique (RSF and RIST) for multistate models (MSRSF and MSRIST) to identify important risk factors in different stages of the diseases. The methods can capture any complex relationship between variables and can be used as a useful tool for identifying important risk factors in different states of this disease.http://link.springer.com/article/10.1186/s12874-018-0596-5HIV/AIDSHighly active antiretroviral therapyRandom forestSurvival analysisRecursively imputed survival treesCohort studies |
| spellingShingle | Leili Tapak Michael R. Kosorok Majid Sadeghifar Omid Hamidi Multistate recursively imputed survival trees for time-to-event data analysis: an application to AIDS and mortality post-HIV infection data BMC Medical Research Methodology HIV/AIDS Highly active antiretroviral therapy Random forest Survival analysis Recursively imputed survival trees Cohort studies |
| title | Multistate recursively imputed survival trees for time-to-event data analysis: an application to AIDS and mortality post-HIV infection data |
| title_full | Multistate recursively imputed survival trees for time-to-event data analysis: an application to AIDS and mortality post-HIV infection data |
| title_fullStr | Multistate recursively imputed survival trees for time-to-event data analysis: an application to AIDS and mortality post-HIV infection data |
| title_full_unstemmed | Multistate recursively imputed survival trees for time-to-event data analysis: an application to AIDS and mortality post-HIV infection data |
| title_short | Multistate recursively imputed survival trees for time-to-event data analysis: an application to AIDS and mortality post-HIV infection data |
| title_sort | multistate recursively imputed survival trees for time to event data analysis an application to aids and mortality post hiv infection data |
| topic | HIV/AIDS Highly active antiretroviral therapy Random forest Survival analysis Recursively imputed survival trees Cohort studies |
| url | http://link.springer.com/article/10.1186/s12874-018-0596-5 |
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