Literature DB >> 14673936

The analysis of asthma control under a Markov assumption with use of covariates.

P Saint-Pierre1, C Combescure, J P Daurès, P Godard.   

Abstract

In studies of disease states and their relation to evolution, data on the state are usually obtained at in frequent time points during follow-up. Moreover in many applications, there are measured covariates on each individual under study and interest centres on the relationship between these covariates and the disease evolution. We developed a continuous-time Markov model with use of time-dependent covariates and a Markov model with piecewise constant intensities to model asthma evolution. Methods to estimate the effect of covariates on transition intensities, to test the assumption of time homogeneity and to assess goodness-of-fit are proposed. We apply these methods to asthma control. We consider a three-state model and we discuss in detail the analysis of asthma control evolution. Copyright 2003 John Wiley & Sons, Ltd.

Mesh:

Year:  2003        PMID: 14673936     DOI: 10.1002/sim.1680

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  14 in total

1.  Using semi-Markov processes to study timeliness and tests used in the diagnostic evaluation of suspected breast cancer.

Authors:  R A Hubbard; J Lange; Y Zhang; B A Salim; J R Stroud; L Y T Inoue
Journal:  Stat Med       Date:  2016-07-21       Impact factor: 2.373

2.  Non-homogeneous Markov process models with informative observations with an application to Alzheimer's disease.

Authors:  Baojiang Chen; Xiao-Hua Zhou
Journal:  Biom J       Date:  2011-04-14       Impact factor: 2.207

3.  A joint model for multistate disease processes and random informative observation times, with applications to electronic medical records data.

Authors:  Jane M Lange; Rebecca A Hubbard; Lurdes Y T Inoue; Vladimir N Minin
Journal:  Biometrics       Date:  2014-10-15       Impact factor: 2.571

4.  Analysis of transtheoretical model of health behavioral changes in a nutrition intervention study--a continuous time Markov chain model with Bayesian approach.

Authors:  Junsheng Ma; Wenyaw Chan; Chu-Lin Tsai; Momiao Xiong; Barbara C Tilley
Journal:  Stat Med       Date:  2015-06-29       Impact factor: 2.373

5.  A Correlated Random Effects Model for Non-homogeneous Markov Processes with Nonignorable Missingness.

Authors:  Baojiang Chen; Xiao-Hua Zhou
Journal:  J Multivar Anal       Date:  2013-05       Impact factor: 1.473

6.  Relationship between risk, cumulative burden of exacerbations and mortality in patients with COPD: modelling analysis using data from the ETHOS study.

Authors:  Kirsty Rhodes; Martin Jenkins; Enrico de Nigris; Magnus Aurivillius; Mario Ouwens
Journal:  BMC Med Res Methodol       Date:  2022-05-25       Impact factor: 4.612

7.  Multi-state models for the analysis of time-to-event data.

Authors:  Luís Meira-Machado; Jacobo de Uña-Alvarez; Carmen Cadarso-Suárez; Per K Andersen
Journal:  Stat Methods Med Res       Date:  2008-06-18       Impact factor: 3.021

8.  A CONTINUOUS-TIME MARKOV CHAIN APPROACH ANALYZING THE STAGES OF CHANGE CONSTRUCT FROM A HEALTH PROMOTION INTERVENTION.

Authors:  Kendra Brown Mhoon; Wenyaw Chan; Deborah J Del Junco; Sally W Vernon
Journal:  JP J Biostat       Date:  2010-10

9.  Bayesian variable selection for multistate Markov models with interval-censored data in an ecological momentary assessment study of smoking cessation.

Authors:  Matthew D Koslovsky; Michael D Swartz; Wenyaw Chan; Luis Leon-Novelo; Anna V Wilkinson; Darla E Kendzor; Michael S Businelle
Journal:  Biometrics       Date:  2017-10-11       Impact factor: 2.571

10.  Continuous time Markov chain approaches for analyzing transtheoretical models of health behavioral change: A case study and comparison of model estimations.

Authors:  Junsheng Ma; Wenyaw Chan; Barbara C Tilley
Journal:  Stat Methods Med Res       Date:  2016-04-04       Impact factor: 3.021

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.