Subjective prior distributions for modeling longitudinal continuous outcomes with non-ignorable dropout.

Abstract

Substance abuse treatment research is complicated by the pervasive problem of non-ignorable missing data-i.e. the occurrence of the missing data is related to the unobserved outcomes. Missing data frequently arise due to early client departure from treatment. Pattern-mixture models (PMMs) are often employed in such situations to jointly model the outcome… (More)
DOI: 10.1002/sim.3484

Cite this paper

@article{Paddock2009SubjectivePD, title={Subjective prior distributions for modeling longitudinal continuous outcomes with non-ignorable dropout.}, author={Susan M. Paddock and Patricia A. Ebener}, journal={Statistics in medicine}, year={2009}, volume={28 4}, pages={659-78} }