Multistate transition modeling: An application using hypertension data
Background & Aim: Multistate models are systems of multivariate survival data where individuals move through a series of istinct states following certain paths of possible transitions. Such models provide a relevant tool for studying observations of a continuous time process at arbitrary times....
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| Format: | Article |
| Language: | English |
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Tehran University of Medical Sciences
2016-12-01
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| Series: | Journal of Biostatistics and Epidemiology |
| Subjects: | |
| Online Access: | https://jbe.tums.ac.ir/index.php/jbe/article/view/82 |
| _version_ | 1818885582814707712 |
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| author | Awol Seid |
| author_facet | Awol Seid |
| author_sort | Awol Seid |
| collection | DOAJ |
| description | Background & Aim: Multistate models are systems of multivariate survival data where individuals move through a series of istinct states following certain paths of possible transitions. Such models provide a relevant tool for studying observations of a continuous time process at arbitrary times. The aim of this study was to model the transitions from a healthy (hypertension free) state to an illness (hypertension) state of a hypertensive patient under treatment.
Methods & Materials: In this article, the application of multistate modeling using hypertension data is demonstrated. Hospital data were obtained for a cohort of 353 patients from Jimma University Hospital, Ethiopia.
Results: Three states of the Markov process are defined based on the WHO guideline of high blood pressure, state 1 (BP < 140/90 mmHg), state 2 (BP ≥ 140/90 mmHg) and state 3 (dropout). The first state is termed as a healthy state, the second an illness state and the third one is an absorbing state. Initially, the state transition intensities and state occupation probabilities are estimated with no covariate. Then, the effect of gender and family history of hypertension on the state transition intensities are evaluated separately and jointly using proportional intensities model.
Conclusion: The study indicates that gender has a significant effect on the transition intensities but not family history ofhypertension. |
| first_indexed | 2024-12-19T16:07:44Z |
| format | Article |
| id | doaj.art-e0bcee1f3fe64715a4ebae9b516f3c7a |
| institution | Directory Open Access Journal |
| issn | 2383-4196 2383-420X |
| language | English |
| last_indexed | 2024-12-19T16:07:44Z |
| publishDate | 2016-12-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Journal of Biostatistics and Epidemiology |
| spelling | doaj.art-e0bcee1f3fe64715a4ebae9b516f3c7a2022-12-21T20:14:49ZengTehran University of Medical SciencesJournal of Biostatistics and Epidemiology2383-41962383-420X2016-12-0122Multistate transition modeling: An application using hypertension dataAwol Seid0Department of Statistics, College of Computing and Informatics, Haramaya University, Dire Dawa, EthiopiaBackground & Aim: Multistate models are systems of multivariate survival data where individuals move through a series of istinct states following certain paths of possible transitions. Such models provide a relevant tool for studying observations of a continuous time process at arbitrary times. The aim of this study was to model the transitions from a healthy (hypertension free) state to an illness (hypertension) state of a hypertensive patient under treatment. Methods & Materials: In this article, the application of multistate modeling using hypertension data is demonstrated. Hospital data were obtained for a cohort of 353 patients from Jimma University Hospital, Ethiopia. Results: Three states of the Markov process are defined based on the WHO guideline of high blood pressure, state 1 (BP < 140/90 mmHg), state 2 (BP ≥ 140/90 mmHg) and state 3 (dropout). The first state is termed as a healthy state, the second an illness state and the third one is an absorbing state. Initially, the state transition intensities and state occupation probabilities are estimated with no covariate. Then, the effect of gender and family history of hypertension on the state transition intensities are evaluated separately and jointly using proportional intensities model. Conclusion: The study indicates that gender has a significant effect on the transition intensities but not family history ofhypertension.https://jbe.tums.ac.ir/index.php/jbe/article/view/82HypertensionMultistate modelingMarkov process |
| spellingShingle | Awol Seid Multistate transition modeling: An application using hypertension data Journal of Biostatistics and Epidemiology Hypertension Multistate modeling Markov process |
| title | Multistate transition modeling: An application using hypertension data |
| title_full | Multistate transition modeling: An application using hypertension data |
| title_fullStr | Multistate transition modeling: An application using hypertension data |
| title_full_unstemmed | Multistate transition modeling: An application using hypertension data |
| title_short | Multistate transition modeling: An application using hypertension data |
| title_sort | multistate transition modeling an application using hypertension data |
| topic | Hypertension Multistate modeling Markov process |
| url | https://jbe.tums.ac.ir/index.php/jbe/article/view/82 |
| work_keys_str_mv | AT awolseid multistatetransitionmodelinganapplicationusinghypertensiondata |