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A random pattern mixture model for ordinal outcomes with informative dropouts
Author(s) -
Liu Chengcheng,
Ratcliffe Sarah J.,
Guo Wensheng
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6514
Subject(s) - dropout (neural networks) , covariate , random effects model , outcome (game theory) , statistics , ordinal regression , ordinal data , mixture model , mathematics , longitudinal data , ordinal scale , econometrics , computer science , machine learning , medicine , data mining , meta analysis , mathematical economics
We extend a random pattern mixture joint model for longitudinal ordinal outcomes and informative dropouts. The patients are generalized to ‘pattern’ groups based on known covariates that are potentially surrogated for the severity of the underlying condition. The random pattern effects are defined as the latent effects linking the dropout process and the ordinal longitudinal outcome. Conditional on the random pattern effects, the longitudinal outcome and the dropout times are assumed independent. Estimates are obtained via the Expectation–maximization algorithm. We applied the model to the end‐stage renal disease data. Anemia was found to be significantly affected by the baseline iron treatment when the dropout information was adjusted via the study model; as opposed to an independent or shared parameter model. Simulations were performed to evaluate the performance of the random pattern mixture model under various assumptions. Copyright © 2015 John Wiley & Sons, Ltd.