Perform Data Fitting with PK/PD Models

SimBiology lets you estimate model parameters by fitting the model to experimental time-course data, using either nonlinear regression or mixed-effects (NLME) techniques. You can perform both individual and population fits to grouped data.

• Individual fit — Fit data using nonlinear regression (least-squares) methods, specify parameter transformations, estimate parameters, and calculate residuals and the estimated coefficient covariance matrix. For a command line workflow, see Fitting Workflow for sbiofit. For an app workflow, see Calculate NCA Parameters and Fit Model to PK/PD Data Using SimBiology Model Analyzer App.

• Population fit — Fit data, specify parameter transformations, and estimate the fixed effects and the random sources of variation on parameters using nonlinear mixed-effects models. For a command line workflow, see Nonlinear Mixed-Effects Modeling Workflow.

• Population fit using a stochastic algorithm — Fit data, specify parameter transformations, and estimate the fixed effects and the random sources of variation on parameters, using the Stochastic Approximation Expectation-Maximization (SAEM) algorithm. SAEM is more robust with respect to starting values. This functionality relaxes assumption of constant error variance. Specify nlmefitsa as the estimation function name when you run sbiofitmixed or select mixed effects using stochastic solver in the Statistical Modeling section of the Fit Data program in the SimBiology Model Analyzer app.

In addition, you can turn on the ProgressPlot option to get the live feedback on the status of parameter estimation.