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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Variability Attribution for Automated Model Building.

Moustafa M A Ibrahim^1,2, Rikard Nordgren¹, Maria C Kjellsson¹

¹Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.

The AAPS Journal

|March 10, 2019

Summary

This summary is machine-generated.

Linearization offers advantages for evaluating residual unexplained variability (RUV) models in automated model building. This method accurately quantifies RUV misspecification and parameter uncertainty, improving upon residual modeling.

Keywords:

automated model building linearization model evaluation nonlinear mixed effects models stochastic model

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Area of Science:

Pharmacometrics
Statistical Modeling
Computational Biology

Background:

Automated model building requires robust methods for evaluating residual unexplained variability (RUV).
Residual modeling is fast but cannot assess the impact of RUV models on parameter precision.
Linearization presents a novel approach to address these limitations.

Purpose of the Study:

To investigate the advantages of linearization for RUV model evaluation in automated model building.
To compare linearization with conventional analysis and residual modeling for RUV assessment.
To evaluate the impact of linearization on parameter variability and uncertainty estimation.

Main Methods:

Six RUV models were tested using 12 real data examples.
Data examples were linearized, and model fit improvements were assessed.
Parameter variabilities and uncertainties from linearization were compared to conventional analysis.

Main Results:

Linearization accurately identified and quantified RUV model misspecification, comparable to residual modeling.
Linearization successfully identified the direction and magnitude of changes in variability parameters and their uncertainties.
The study demonstrated the utility of linearization for automated model building and evaluation.

Conclusions:

Linearization provides a valuable method for evaluating RUV models in automated model building.
This technique enhances the assessment of parameter imprecision caused by RUV models.
Linearization is implemented in the PsN software package for continuous data analysis.