Literature DB >> 15236426

Analysis of longitudinal studies with death and drop-out: a case study.

Carole Dufouil1, Carol Brayne, David Clayton.   

Abstract

The analysis of longitudinal data has recently been an active area of biostatistical research. Two main approaches to analysis have emerged, the first concentrating on modelling evolution of marginal distributions of the main response variable of interest and the other on subject-specific trajectories. In epidemiology the analysis is usually complicated by missing data and by death of study participants. Motivated by a study of cognitive decline in the elderly, this paper argues that these two types of incomplete follow-up may need to be treated differently in the analysis, and proposes an extension to the marginal modelling approach. The problem of informative drop-out is also discussed. The methods are implemented in the 'Stata' statistical package. Copyright 2004 John Wiley & Sons, Ltd.

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Year:  2004        PMID: 15236426     DOI: 10.1002/sim.1821

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


  49 in total

1.  Sex differences in the level and rate of change of physical function and grip strength in the Danish 1905-cohort study.

Authors:  Anna Oksuzyan; Heiner Maier; Matt McGue; James W Vaupel; Kaare Christensen
Journal:  J Aging Health       Date:  2010-05-07

2.  Longitudinal Data with Follow-up Truncated by Death: Match the Analysis Method to Research Aims.

Authors:  Brenda F Kurland; Laura L Johnson; Brian L Egleston; Paula H Diehr
Journal:  Stat Sci       Date:  2009       Impact factor: 2.901

3.  Joint modeling of missing data due to non-participation and death in longitudinal aging studies.

Authors:  Kumar B Rajan; Sue E Leurgans
Journal:  Stat Med       Date:  2010-09-20       Impact factor: 2.373

4.  Factors influencing attrition in 35 Alzheimer's Disease Centers across the USA: a longitudinal examination of the National Alzheimer's Coordinating Center's Uniform Data Set.

Authors:  Shanna L Burke; Tianyan Hu; Mitra Naseh; Nicole M Fava; Janice O'Driscoll; Daniel Alvarez; Linda B Cottler; Ranjan Duara
Journal:  Aging Clin Exp Res       Date:  2018-12-10       Impact factor: 3.636

5.  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

6.  Evaluating Convergence of Within-Person Change and Between-Person Age Differences in Age-Heterogeneous Longitudinal Studies.

Authors:  Martin Sliwinski; Lesa Hoffman; Scott M Hofer
Journal:  Res Hum Dev       Date:  2010-01

7.  Brain MRI markers and dropout in a longitudinal study of cognitive aging: the Three-City Dijon Study.

Authors:  M Maria Glymour; Geneviève Chêne; Christophe Tzourio; Carole Dufouil
Journal:  Neurology       Date:  2012-09-12       Impact factor: 9.910

8.  Weighted estimating equations for longitudinal studies with death and non-monotone missing time-dependent covariates and outcomes.

Authors:  Michelle Shardell; Ram R Miller
Journal:  Stat Med       Date:  2008-03-30       Impact factor: 2.373

9.  Estimating inverse-probability weights for longitudinal data with dropout or truncation: The xtrccipw command.

Authors:  Eric J Daza; Michael G Hudgens; Amy H Herring
Journal:  Stata J       Date:  2017 2nd Quarter       Impact factor: 2.637

10.  A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials.

Authors:  Ian R White; James Carpenter; Nicholas J Horton
Journal:  Stat Sin       Date:  2018-10       Impact factor: 1.261

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