Literature DB >> 27123856

Joint latent class model for longitudinal data and interval-censored semi-competing events: Application to dementia.

Anaïs Rouanet1,2, Pierre Joly1, Jean-François Dartigues1,2, Cécile Proust-Lima1,2, Hélène Jacqmin-Gadda1,2.   

Abstract

Joint models are used in ageing studies to investigate the association between longitudinal markers and a time-to-event, and have been extended to multiple markers and/or competing risks. The competing risk of death must be considered in the elderly because death and dementia have common risk factors. Moreover, in cohort studies, time-to-dementia is interval-censored since dementia is assessed intermittently. So subjects can develop dementia and die between two visits without being diagnosed. To study predementia cognitive decline, we propose a joint latent class model combining a (possibly multivariate) mixed model and an illness-death model handling both interval censoring (by accounting for a possible unobserved transition to dementia) and semi-competing risks. Parameters are estimated by maximum-likelihood handling interval censoring. The correlation between the marker and the times-to-events is captured by latent classes, homogeneous sub-groups with specific risks of death, dementia, and profiles of cognitive decline. We propose Markovian and semi-Markovian versions. Both approaches are compared to a joint latent-class model for competing risks through a simulation study, and applied in a prospective cohort study of cerebral and functional ageing to distinguish different profiles of cognitive decline associated with risks of dementia and death. The comparison highlights that among subjects with dementia, mortality depends more on age than on duration of dementia. This model distinguishes the so-called terminal predeath decline (among healthy subjects) from the predementia decline.
© 2016, The International Biometric Society.

Entities:  

Keywords:  Illness-death; Interval censoring; Joint model; Mixed model; Semi-competing risks

Mesh:

Year:  2016        PMID: 27123856     DOI: 10.1111/biom.12530

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  7 in total

1.  Dealing with death when studying disease or physiological marker: the stochastic system approach to causality.

Authors:  Daniel Commenges
Journal:  Lifetime Data Anal       Date:  2018-11-17       Impact factor: 1.588

2.  Modelling of viral load dynamics and CD4 cell count progression in an antiretroviral naive cohort: using a joint linear mixed and multistate Markov model.

Authors:  Zelalem G Dessie; Temesgen Zewotir; Henry Mwambi; Delia North
Journal:  BMC Infect Dis       Date:  2020-03-26       Impact factor: 3.090

3.  Medications use among women with dementia: a cohort study.

Authors:  Kailash Thapaliya; Melissa L Harris; Peta M Forder; Julie E Byles
Journal:  Aging Clin Exp Res       Date:  2021-05-26       Impact factor: 3.636

4.  Comparison of a time-varying covariate model and a joint model of time-to-event outcomes in the presence of measurement error and interval censoring: application to kidney transplantation.

Authors:  Kristen R Campbell; Elizabeth Juarez-Colunga; Gary K Grunwald; James Cooper; Scott Davis; Jane Gralla
Journal:  BMC Med Res Methodol       Date:  2019-06-26       Impact factor: 4.615

5.  Hidden three-state survival model for bivariate longitudinal count data.

Authors:  Ardo van den Hout; Graciela Muniz-Terrera
Journal:  Lifetime Data Anal       Date:  2018-08-27       Impact factor: 1.588

6.  Integrating latent classes in the Bayesian shared parameter joint model of longitudinal and survival outcomes.

Authors:  Eleni-Rosalina Andrinopoulou; Kazem Nasserinejad; Rhonda Szczesniak; Dimitris Rizopoulos
Journal:  Stat Methods Med Res       Date:  2020-05-21       Impact factor: 3.021

Review 7.  Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer's Dementia Longitudinal Cohort.

Authors:  James Howlett; Steven M Hill; Craig W Ritchie; Brian D M Tom
Journal:  Front Big Data       Date:  2021-08-20
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.