Use of multi-state models for time-to-event data

Background & Aim: In medical sciences, the outcome is the time until the occurrence of an event of interest. A multi-state model (MSM) is used to model a process where subjects’ transition takes place from one state to the next. For instance, a standard survival curve can be thought of as a sim...

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Main Authors: Mahtab Rouhifard, Mehdi Yaseri
Format: Article
Language:English
Published: Tehran University of Medical Sciences 2018-07-01
Series:Journal of Biostatistics and Epidemiology
Subjects:
Online Access:https://jbe.tums.ac.ir/index.php/jbe/article/view/181
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spelling doaj-2c898ebc97a14123b653bff921b0f2c82020-12-06T04:15:05ZengTehran University of Medical SciencesJournal of Biostatistics and Epidemiology2383-41962383-420X2018-07-0133/4Use of multi-state models for time-to-event dataMahtab Rouhifard0Mehdi Yaseri1Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Background & Aim: In medical sciences, the outcome is the time until the occurrence of an event of interest. A multi-state model (MSM) is used to model a process where subjects’ transition takes place from one state to the next. For instance, a standard survival curve can be thought of as a simple MSM with two states (alive and dead) and the transition between these two-state models is a method used to analyze time to event data. The most important aspect of this model is that it considers intermediate events and models the effect of covariates on each transition intensity. Some diseases like cancer, human immunodeficiency virus (HIV), etc. have several stages. In the present study, these models were reviewed using cardiac allograft vasculopathy (CAV) data focusing on different approaches. Methods & Materials: The data of 576 CAVs were collected. A time dependent simple Cox regression model (CRM) was fitted and a three-state illness-death model was considered for the MSMs. Results: In the simple CRM, only the individuals with the age of > 50 were significant, however for Cox Markov model (CMM) and Cox semi-Markov model (CSMM), the donor’s age > 40, sex, and the individuals with the age of > 50 were other significant covariates. Conclusion: The CMM and CSMM showed more accurate results about risk factors compared to the simple CRM. https://jbe.tums.ac.ir/index.php/jbe/article/view/181Markov chainsSurvival analysisRisk factorsProportional hazards modelsDisease progression
collection DOAJ
language English
format Article
sources DOAJ
author Mahtab Rouhifard
Mehdi Yaseri
spellingShingle Mahtab Rouhifard
Mehdi Yaseri
Use of multi-state models for time-to-event data
Journal of Biostatistics and Epidemiology
Markov chains
Survival analysis
Risk factors
Proportional hazards models
Disease progression
author_facet Mahtab Rouhifard
Mehdi Yaseri
author_sort Mahtab Rouhifard
title Use of multi-state models for time-to-event data
title_short Use of multi-state models for time-to-event data
title_full Use of multi-state models for time-to-event data
title_fullStr Use of multi-state models for time-to-event data
title_full_unstemmed Use of multi-state models for time-to-event data
title_sort use of multi-state models for time-to-event data
publisher Tehran University of Medical Sciences
series Journal of Biostatistics and Epidemiology
issn 2383-4196
2383-420X
publishDate 2018-07-01
description Background & Aim: In medical sciences, the outcome is the time until the occurrence of an event of interest. A multi-state model (MSM) is used to model a process where subjects’ transition takes place from one state to the next. For instance, a standard survival curve can be thought of as a simple MSM with two states (alive and dead) and the transition between these two-state models is a method used to analyze time to event data. The most important aspect of this model is that it considers intermediate events and models the effect of covariates on each transition intensity. Some diseases like cancer, human immunodeficiency virus (HIV), etc. have several stages. In the present study, these models were reviewed using cardiac allograft vasculopathy (CAV) data focusing on different approaches. Methods & Materials: The data of 576 CAVs were collected. A time dependent simple Cox regression model (CRM) was fitted and a three-state illness-death model was considered for the MSMs. Results: In the simple CRM, only the individuals with the age of > 50 were significant, however for Cox Markov model (CMM) and Cox semi-Markov model (CSMM), the donor’s age > 40, sex, and the individuals with the age of > 50 were other significant covariates. Conclusion: The CMM and CSMM showed more accurate results about risk factors compared to the simple CRM.
topic Markov chains
Survival analysis
Risk factors
Proportional hazards models
Disease progression
url https://jbe.tums.ac.ir/index.php/jbe/article/view/181
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