Literature DB >> 20161282

Effects of ignoring baseline on modeling transitions from intact cognition to dementia.

Lei Yu1, Suzanne L Tyas, David A Snowdon, Richard J Kryscio.   

Abstract

This paper evaluates the effect of ignoring baseline when modeling transitions from intact cognition to dementia with mild cognitive impairment (MCI) and global impairment (GI) as intervening cognitive states. Transitions among states are modeled by a discrete-time Markov chain having three transient (intact cognition, MCI, and GI) and two competing absorbing states (death and dementia). Transition probabilities depend on two covariates, age and the presence/absence of an apolipoprotein E-epsilon4 allele, through a multinomial logistic model with shared random effects. Results are illustrated with an application to the Nun Study, a cohort of 678 participants 75+ years of age at baseline and followed longitudinally with up to ten cognitive assessments per nun.

Entities:  

Year:  2009        PMID: 20161282      PMCID: PMC2703484          DOI: 10.1016/j.csda.2009.02.007

Source DB:  PubMed          Journal:  Comput Stat Data Anal        ISSN: 0167-9473            Impact factor:   1.681


  11 in total

1.  Mixed effects logistic regression models for longitudinal ordinal functional response data with multiple-cause drop-out from the longitudinal study of aging.

Authors:  T R Ten Have; M E Miller; B A Reboussin; M K James
Journal:  Biometrics       Date:  2000-03       Impact factor: 2.571

2.  Mixed effects logistic regression models for multiple longitudinal binary functional limitation responses with informative drop-out and confounding by baseline outcomes.

Authors:  HaveThomasR Ten; Beth A Reboussin; Michael E Miller; Allen Kunselman
Journal:  Biometrics       Date:  2002-03       Impact factor: 2.571

3.  Marginally specified logistic-normal models for longitudinal binary data.

Authors:  P J Heagerty
Journal:  Biometrics       Date:  1999-09       Impact factor: 2.571

4.  A shared random effect parameter approach for longitudinal dementia data with non-ignorable missing data.

Authors:  Sujuan Gao
Journal:  Stat Med       Date:  2004-01-30       Impact factor: 2.373

5.  Type I and Type II error under random-effects misspecification in generalized linear mixed models.

Authors:  Saskia Litière; Ariel Alonso; Geert Molenberghs
Journal:  Biometrics       Date:  2007-04-09       Impact factor: 2.571

6.  Shared random effects analysis of multi-state Markov models: application to a longitudinal study of transitions to dementia.

Authors:  Juan C Salazar; Frederick A Schmitt; Lei Yu; Marta M Mendiondo; Richard J Kryscio
Journal:  Stat Med       Date:  2007-02-10       Impact factor: 2.373

7.  Mixed effects logistic regression models for longitudinal binary response data with informative drop-out.

Authors:  T R Ten Have; A R Kunselman; E P Pulkstenis; J R Landis
Journal:  Biometrics       Date:  1998-03       Impact factor: 2.571

8.  Random-effects models for serial observations with binary response.

Authors:  R Stiratelli; N Laird; J H Ware
Journal:  Biometrics       Date:  1984-12       Impact factor: 2.571

9.  Brain infarction and the clinical expression of Alzheimer disease. The Nun Study.

Authors:  D A Snowdon; L H Greiner; J A Mortimer; K P Riley; P A Greiner; W R Markesbery
Journal:  JAMA       Date:  1997-03-12       Impact factor: 56.272

10.  Transitions to mild cognitive impairments, dementia, and death: findings from the Nun Study.

Authors:  Suzanne L Tyas; Juan Carlos Salazar; David A Snowdon; Mark F Desrosiers; Kathryn P Riley; Marta S Mendiondo; Richard J Kryscio
Journal:  Am J Epidemiol       Date:  2007-04-12       Impact factor: 4.897

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  5 in total

1.  Markov Transition Model to Dementia with Death as a Competing Event.

Authors:  Shaoceng Wei; Liou Xu; Richard J Kryscio
Journal:  Comput Stat Data Anal       Date:  2014-12-01       Impact factor: 1.681

2.  A nonstationary Markov transition model for computing the relative risk of dementia before death.

Authors:  Lei Yu; William S Griffith; Suzanne L Tyas; David A Snowdon; Richard J Kryscio
Journal:  Stat Med       Date:  2010-03-15       Impact factor: 2.373

3.  Analysis of combined incident and prevalent cohort data under a proportional mean residual life model.

Authors:  Chi Hyun Lee; Jing Ning; Richard J Kryscio; Yu Shen
Journal:  Stat Med       Date:  2019-01-24       Impact factor: 2.373

4.  Are Markov and semi-Markov models flexible enough for cognitive panel data?

Authors:  Richard J Kryscio; Erin L Abner
Journal:  J Biom Biostat       Date:  2013-01-01

5.  Adjusting for mortality when identifying risk factors for transitions to mild cognitive impairment and dementia.

Authors:  Richard J Kryscio; Erin L Abner; Yushun Lin; Gregory E Cooper; David W Fardo; Gregory A Jicha; Peter T Nelson; Charles D Smith; Linda J Van Eldik; Lijie Wan; Frederick A Schmitt
Journal:  J Alzheimers Dis       Date:  2013       Impact factor: 4.160

  5 in total

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