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Parameter Fitting in Pharmacokinetic Models

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Parameter Fitting Functionality Prerequisites for Parameter Fitting

Parameter Fitting Functionality

SimBiology lets you perform individual fits and population fits on grouped data. This functionality uses features in Statistics Toolbox (Version 7.0 or later).

There are two classes of methods available for population fitting:

• Methods that directly estimate parameters using the likelihood function namely, LME and RelME

• Methods that linearize the likelihood function, namely, first-order approximation (FO) and first-order approximation at the conditional estimates 'FOCE'

The following results are returned:

• The maximized log-likelihood for the fitted model

• The estimated error variance for the fitted model

• The Akaike information criterion for the fitted model

• The Bayesian information criterion for the fitted model

• The standard errors for the estimates of the fixed effects

• The error degrees of freedom for the model

In addition, you can generate diagnostic plots that show:

• The predicted time courses and observations for an individual or the population

• Observed versus predicted values

• Residuals versus time, group, or predictions

• Distribution of the residuals

• A box-plot for random effects or parameter estimates from individual fitting

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Prerequisites for Parameter Fitting

Before you fit parameters, the SimBiology desktop, or the MATLAB Workspace must contain the following:

• Data to use in the fitting (see Importing Data for more information)

• A model fit (see Creating Pharmacokinetic Models for more information)

Depending on whether you plan to use the command line or the SimBiology desktop, see the following for more information.

• Fitting Pharmacokinetic Model Parameters at the Command Line

• Fitting Pharmacokinetic Model Parameters in the SimBiology Desktop

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Parameter Fitting Using Custom SimBiology Models

Fitting Pharmacokinetic Model Parameters at the Command Line

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