Mixed Effects Models for the Population Approach (2014)

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Features of Mixed Effects Models for the Population Approach (2014) PDF free download:

Population models describe biological and physical phenomena observed in each of a set of individuals, and also the variability between individuals. This approach finds its place in domains like pharmacometrics when we need to quantitatively describe interactions between diseases, drugs and patients. This means developing models that take into account that different patients react differently to the same disease and the same drug.
The adoption of the population approach in pharmacometrics is largely due to the creation of the NONMEM software in the late 1970s which enabled the use of nonlinear mixed effects models for pharmacometric data analysis. Indeed, the population approach can be formulated in statistical terms using mixed effects models. While linear mixed effects models have been around and widely used since the 1950s, nonlinear mixed effects models remain rarely used even in areas where they are particularly well-suited such as biology, agronomy, econometrics, environmental and human sciences.

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