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An Alternative Parameterization of the General Linear Mixture Model for Longitudinal Data with Non‐ignorable Drop‐outs by Garrett M. Fitzmaurice; Nan M. Laird; Lucy Shneyer is a Mathematics article available to read on EtoBox.

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## Abstract This paper considers the mixture model methodology for handling non‐ignorable drop‐outs in longitudinal studies with continuous outcomes. Recently, Hogan and Laird have developed a mixture model for non‐ignorable drop‐outs which is a standard linear mixed effects model except that the parameters which characterize change over time depend also upon time of drop‐out. That is, the mean response is linear in time, other covariates and drop‐out time, and their interactions. One of the key attractions of the mixture modelling approach to drop‐outs is that it is relatively easy to explore the sensitivity of results to model specification. However, the main drawback of mixture models is that the parameters that are ordinarily of interest are not immediately available, but require marginalization of the distribution of outcome over drop‐out times. Furthermore, although a linear model is assumed for the conditional mean of the outcome vector given time of drop out, after marginalization, the unconditional mean of the outcome vector is not, in general, linear in the regression parameters. As a result, it is not possible to parsimoniously describe the effects of covariates on the m

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Author

Garrett M. Fitzmaurice; Nan M. Laird; Lucy Shneyer

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2001

Language

EN

Field

Mathematics (Physical Sciences)