A bidimensional finite mixture model for longitudinal data subject to dropout.
In longitudinal studies, subjects may be lost to follow up and, thus, present incomplete response sequences. When the mechanism underlying the dropout is nonignorable, we need to account for dependence between the longitudinal and the dropout process. We propose to model such a dependence through di...
| Publicado en: | Statistics in Medicine Vol. 37; no. 20; pp. 2998 - 3012 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
9/10/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131152278&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131152278 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 9/10/2018 vid: 37 iid: 20 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 131152278 131152278 NLM29873102 131152278 10.1002/sim.7698 NLM29873102 131152278 ppf: 2998 ppct: 14 formats: tig: atl: A bidimensional finite mixture model for longitudinal data subject to dropout. aug: au: Spagnoli, Alessandra Marino, Maria Francesca Alfò, Marco affil: Dipartimento di Sanità Pubblica e Malattie Infettive, Sapienza Università di Roma, Rome, Italy sug: subj: Cognition Disorders Models, Statistical Prospective Studies Research Subjects Netherlands Female Male Algorithms Aged, 80 and Over Human Aged, 80 & over Female Male ab: In longitudinal studies, subjects may be lost to follow up and, thus, present incomplete response sequences. When the mechanism underlying the dropout is nonignorable, we need to account for dependence between the longitudinal and the dropout process. We propose to model such a dependence through discrete latent effects, which are outcome-specific and account for heterogeneity in the univariate profiles. Dependence between profiles is introduced by using a probability matrix to describe the corresponding joint distribution. In this way, we separately model dependence within each outcome and dependence between outcomes. The major feature of this proposal, when compared with standard finite mixture models, is that it allows the nonignorable dropout model to properly nest its ignorable counterpart. We also discuss the use of an index of (local) sensitivity to nonignorability to investigate the effects that assumptions about the dropout process may have on model parameter estimates. The proposal is illustrated via the analysis of data from a longitudinal study on the dynamics of cognitive functioning in the elderly. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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