Sentence examples for mixed effects modeling from inspiring English sources

This analysis characterizes and evaluates the population PK of fluvoxamine in rat plasma using nonlinear mixed effects modeling.

Mixed effects modeling is a valuable tool in mass spectrometry-based proteomics research.

Mixed effects modeling methodology can be easily expanded to apply to ensemble data from different platforms.

As is shown above, mixed effects modeling provides powerful inference to this dataset.

Data were analyzed by nonlinear mixed effects modeling in NONMEM, version 7.2.0.

The mixed effects modeling (MEM) method represents the closest existing approach for information pooling.

Generalized linear mixed-effects modeling is a longitudinal technique that analyzes individual trajectories and produces correlates.

Furthermore, we introduced mixed-distribution mixed-effects modeling to analyze time-activity data in which a large percentage of participants are assigned a zero value.

Venous and capillary plasma concentrations were transformed into their natural logarithms and modeled simultaneously using nonlinear mixed-effects modeling.

Accordingly, the classical nonlinear mixed-effects modeling approach becomes futile under this scenario.

Model development was performed using nonlinear mixed-effects modeling.