Longitudinal and time‐to‐drop‐out joint models can lead to seriously biased estimates when the drop‐out mechanism is at random

DOI10.1111/BIOM.12986zbMath1436.62645OpenAlexW2898265081WikidataQ57784722 ScholiaQ57784722MaRDI QIDQ5214442

Christos Thomadakis, Loukia Meligkotsidou, Giota Touloumi, Nikos Pantazis

Publication date: 7 February 2020

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.12986

zbMATH Keywords

asymptotic biasMCMCjoint modelingmissingnessMAR drop-outshared random effects models

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of statistics to biology and medical sciences; meta analysis (62P10) Missing data (62D10)

Related Items (3)

Predictors of refraction prediction error after cataract surgery: a shared parameter model to account for missing post-operative measurements ⋮ Biased estimation with shared parameter models in the presence of competing dropout mechanisms ⋮ Rejoinder to “Biased Estimation With Shared Parameter Models in the Presence of Competing Dropout Mechanisms”

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