A random pattern mixture model for ordinal outcomes with informative dropouts |
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| Authors: | Chengcheng Liu Sarah J Ratcliffe Wensheng Guo |
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| Affiliation: | 1. Allergan, Inc., Bridgewater, NJ, U.S.A.;2. Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, U.S.A. |
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| Abstract: | 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. |
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| Keywords: | adaptive gaussian quadrature EM algorithm Newton– Raphson algorithm Pattern mixture model ordinal outcome |
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