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Non-linear Models for Longitudinal Data.


ABSTRACT: While marginal models, random-effects models, and conditional models are routinely considered to be the three main modeling families for continuous and discrete repeated measures with linear and generalized linear mean structures, respectively, it is less common to consider non-linear models, let alone frame them within the above taxonomy. In the latter situation, indeed, when considered at all, the focus is often exclusively on random-effects models. In this paper, we consider all three families, exemplify their great flexibility and relative ease of use, and apply them to a simple but illustrative set of data on tree circumference growth of orange trees.

SUBMITTER: Serroyen J 

PROVIDER: S-EPMC2774254 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Non-linear Models for Longitudinal Data.

Serroyen Jan J   Molenberghs Geert G   Verbeke Geert G   Davidian Marie M  

The American statistician 20091101 4


While marginal models, random-effects models, and conditional models are routinely considered to be the three main modeling families for continuous and discrete repeated measures with linear and generalized linear mean structures, respectively, it is less common to consider non-linear models, let alone frame them within the above taxonomy. In the latter situation, indeed, when considered at all, the focus is often exclusively on random-effects models. In this paper, we consider all three familie  ...[more]

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