A latent-class mixture model for incomplete longitudinal Gaussian data |
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Authors: | Beunckens Caroline Molenberghs Geert Verbeke Geert Mallinckrodt Craig |
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Institution: | Center for Statistics, Hasselt University, Agoralaan 1, 3590 Diepenbeek, Belgium;Biostatistical Centre, Catholic University of Leuven, Kapucijnenvoer 35, 3000 Leuven, Belgium;Eli Lilly &Company, Lilly Corporate Center, Indianapolis, Indiana 46285, U.S.A. |
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Abstract: | Summary . In the analyses of incomplete longitudinal clinical trial data, there has been a shift, away from simple methods that are valid only if the data are missing completely at random, to more principled ignorable analyses, which are valid under the less restrictive missing at random assumption. The availability of the necessary standard statistical software nowadays allows for such analyses in practice. While the possibility of data missing not at random (MNAR) cannot be ruled out, it is argued that analyses valid under MNAR are not well suited for the primary analysis in clinical trials. Rather than either forgetting about or blindly shifting to an MNAR framework, the optimal place for MNAR analyses is within a sensitivity-analysis context. One such route for sensitivity analysis is to consider, next to selection models, pattern-mixture models or shared-parameter models. The latter can also be extended to a latent-class mixture model, the approach taken in this article. The performance of the so-obtained flexible model is assessed through simulations and the model is applied to data from a depression trial. |
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Keywords: | Latent class Nonrandom missingness Random effect Shared parameter |
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