Literature DB >> 21415017

Joint model with latent state for longitudinal and multistate data.

E Dantan1, P Joly, J-F Dartigues, H Jacqmin-Gadda.   

Abstract

In many chronic diseases, the patient's health status is followed up by quantitative markers. The evolution is often characterized by a 2-phase degradation process, that is, a normal phase followed by a pathological degradation phase preceding the disease diagnosis. We propose a joint multistate model with latent state for the joint modeling of repeated measures of a quantitative marker, time-to-illness and time-to-death. Using data from the PAQUID cohort on cognitive aging, we jointly studied cognitive decline, dementia risk, and death risk. We estimated the mean evolution of cognitive scores given age at dementia for subjects alive and demented, the mean evolution of cognitive scores for subjects alive and nondemented, in addition to age at acceleration of cognitive decline and duration of the pre-dementia phase.

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Year:  2011        PMID: 21415017     DOI: 10.1093/biostatistics/kxr003

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  9 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.  Joint modelling of longitudinal and multi-state processes: application to clinical progressions in prostate cancer.

Authors:  Loïc Ferrer; Virginie Rondeau; James Dignam; Tom Pickles; Hélène Jacqmin-Gadda; Cécile Proust-Lima
Journal:  Stat Med       Date:  2016-04-18       Impact factor: 2.373

3.  Shared parameter models for joint analysis of longitudinal and survival data with left truncation due to delayed entry - Applications to cystic fibrosis.

Authors:  Mark D Schluchter; Annalisa V Piccorelli
Journal:  Stat Methods Med Res       Date:  2018-04-04       Impact factor: 3.021

4.  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

Review 5.  Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis.

Authors:  Maria Sudell; Ruwanthi Kolamunnage-Dona; Catrin Tudur-Smith
Journal:  BMC Med Res Methodol       Date:  2016-12-05       Impact factor: 4.615

6.  Joint modelling of longitudinal and survival data: incorporating delayed entry and an assessment of model misspecification.

Authors:  Michael J Crowther; Therese M-L Andersson; Paul C Lambert; Keith R Abrams; Keith Humphreys
Journal:  Stat Med       Date:  2015-10-29       Impact factor: 2.373

7.  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

8.  Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer's disease with Fortasyn Connect.

Authors:  Floor M van Oudenhoven; Sophie H N Swinkels; Tobias Hartmann; Hilkka Soininen; Anneke M J van Hees; Dimitris Rizopoulos
Journal:  BMC Med Res Methodol       Date:  2019-07-25       Impact factor: 4.615

9.  Critical examination of current response shift methods and proposal for advancing new methods.

Authors:  Véronique Sébille; Lisa M Lix; Olawale F Ayilara; Tolulope T Sajobi; A Cecile J W Janssens; Richard Sawatzky; Mirjam A G Sprangers; Mathilde G E Verdam
Journal:  Qual Life Res       Date:  2021-02-17       Impact factor: 4.147

  9 in total

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