NDLI: A marginalized conditional linear model for longitudinal binary data when informative dropout occurs in continuous time

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A marginalized conditional linear model for longitudinal binary data when informative dropout occurs in continuous time

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A marginalized conditional linear model for longitudinal binary data when informative dropout occurs in continuous time

Content Provider	PubMed Central
Author	Su, Li
Copyright Year	2012
Abstract	Within the pattern-mixture modeling framework for informative dropout, conditional linear models (CLMs) are a useful approach to deal with dropout that can occur at any point in continuous time (not just at observation times). However, in contrast with selection models, inferences about marginal covariate effects in CLMs are not readily available if nonidentity links are used in the mean structures. In this article, we propose a CLM for long series of longitudinal binary data with marginal covariate effects directly specified. The association between the binary responses and the dropout time is taken into account by modeling the conditional mean of the binary response as well as the dependence between the binary responses given the dropout time. Specifically, parameters in both the conditional mean and dependence models are assumed to be linear or quadratic functions of the dropout time; and the continuous dropout time distribution is left completely unspecified. Inference is fully Bayesian. We illustrate the proposed model using data from a longitudinal study of depression in HIV-infected women, where the strategy of sensitivity analysis based on the extrapolation method is also demonstrated.
Related Links	http://dx.doi.org/10.1093/biostatistics/kxr041
Ending Page	368
Page Count	14
Starting Page	355
File Format	PDF
ISSN	14654644
e-ISSN	14684357
Journal	Biostatistics (Oxford, England)
Issue Number	2
Volume Number	13
Language	English
Publisher	Oxford University Press
Publisher Date	2012-04-01
Access Restriction	Open
Rights Holder	Oxford University Press
Subject Keyword	Statistics, Probability and Uncertainty Statistics and Probability Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Medicine Statistics, Probability and Uncertainty

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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