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BAYESIAN MODELING LONGITUDINAL DYADIC DATA WITH NONIGNORABLE DROPOUT, WITH APPLICATION TO A BREAST CANCER STUDY

Dyadic data are common in the social and behavioral sciences, in which members of dyads are correlated due to the interdependence structure within dyads. The analysis of longitudinal dyadic data becomes complex when nonignorable dropouts occur. We propose a fully Bayesian selection-model-based appro...

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Autori principali: Zhang, Guangyu, Yuan, Ying
Natura: Artigo
Lingua:Inglês
Pubblicazione: 2012
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Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC3693094/
https://ncbi.nlm.nih.gov/pubmed/23814631
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1214/11-AOAS515
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