NDLI: Handling non-ignorable dropouts in longitudinal data: a conditional model based on a latent Markov heterogeneity structure

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Handling non-ignorable dropouts in longitudinal data: a conditional model based on a latent Markov heterogeneity structure

Content Provider	Springer Nature Link
Author	Maruotti, Antonello
Copyright Year	2014
Abstract	We illustrate a class of conditional models for the analysis of longitudinal data suffering attrition in random effects models framework, where the subject-specific random effects are assumed to be discrete and to follow a time-dependent latent process. The latent process accounts for unobserved heterogeneity and correlation between individuals in a dynamic fashion, and for dependence between the observed process and the missing data mechanism. Of particular interest is the case where the missing mechanism is non-ignorable. To deal with the topic we introduce a conditional to dropout model. A shape change in the random effects distribution is considered by directly modeling the effect of the missing data process on the evolution of the latent structure. To estimate the resulting model, we rely on the conditional maximum likelihood approach and for this aim we outline an EM algorithm. The proposal is illustrated via simulations and then applied on a dataset concerning skin cancers. Comparisons with other well-established methods are provided as well.
Starting Page	84
Ending Page	109
Page Count	26
File Format	PDF
ISSN	11330686
Journal	Test
Volume Number	24
Issue Number	1
e-ISSN	18638260
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-08-24
Publisher Institution	Spanish Society of Statistics and Operations Research
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hidden Markov chains Conditional maximum likelihood Non-ignorable missingness Longitudinal data Skin cancer Generalized linear models Applications of Markov chains and discrete-time Markov processes on general state spaces Applications to biology and medical sciences Statistics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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