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A characterization of missingness at random in a generalized shared‐parameter joint modeling framework for longitudinal and time‐to‐event data, and sensitivity analysis
Author(s) -
Njagi Edmund Njeru,
Molenberghs Geert,
Kenward Michael G.,
Verbeke Geert,
Rizopoulos Dimitris
Publication year - 2014
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201300028
Subject(s) - missing data , random effects model , event (particle physics) , computer science , econometrics , statistics , joint (building) , event data , marginal model , data mining , mathematics , covariate , regression analysis , engineering , medicine , architectural engineering , meta analysis , physics , quantum mechanics
We consider a conceptual correspondence between the missing data setting, and joint modeling of longitudinal and time‐to‐event outcomes. Based on this, we formulate an extended shared random effects joint model. Based on this, we provide a characterization of missing at random, which is in line with that in the missing data setting. The ideas are illustrated using data from a study on liver cirrhosis, contrasting the new framework with conventional joint models.