Package: PFIM 8.0.1

Overview | Design evaluation | PK model from the library of models | Define the PK model Linear1InfusionSingleDose_ClV from the library of models | Model parameters | Define mu and omega for each parameter | Residual error model | Define the error model to the response PK RespPK | Administration parameters | Define the administration parameters of the response PK | Sampling times | Define the sampling times for the response PK RespPK | Arm and design | Define an arm called arm1 of size 150 with administration administrationRespPK and samplings samplingTimesRespPK | Add the arm arm1 to the design design1 | FIM types: population, individual, and Bayesian | Evaluate the population, individual and Bayesian FIMs | Display the results of the design evaluations | Create and save the report for the design evaluation | Design optimization | Continuous design space | Create sampling times for the initial design | Define design constraints | Create the constraint arm and the associated design | PSO algorithm (Particle Swarm Optimization) | Set the parameters of the PSO algorithm | Run the PSO algorithm for the optimization with a population FIM | Display the results of the design optimization | Create and save the report for the design optimization | PGBO algorithm (Population genetic-based optimization) | Set the parameters of the PGBO algorithm | Run the PGBO algorithm for the optimization with a population FIM | Simplex algorithm (Nelder–Mead) | Set the parameters of the Simplex algorithm | Run the Simplex algorithm for the optimization with a population FIM | References

Overview | Original experimental design | Objectives | Design evaluation | Model equations | Model parameters | Residual error models | Sampling times | Arms (dose groups) | Design assembly | Population and Bayesian FIM evaluation | Population FIM | Bayesian FIM | Results: display and export | Diagnostic plots | HTML report | Design optimization | Objectives and constraints | Algorithms compared | Runtime note | Initial administration and candidate sampling grids | Sampling time constraints | Initial elementary protocols for Fedorov-Wynn | Dose constraints | Constrained arm and design | Fedorov-Wynn algorithm | Display and plot Fedorov-Wynn results | Multiplicative algorithm | Display and plot Multiplicative algorithm results | References

Overview | Experimental design | Objectives | Design evaluation | PK model user-defined | Define the PK model equation | Model parameters | Define mu, omega and gamma for each parameter | Residual error model | Define the error model to the response PK RespPK | Covariates | Define the between-subject covariate | Define the within-subject covariate | Administration and sampling times | Define the administration parameters and the sampling times of the response PK | Arm and design | Define an arm called arm1 of size 40 encompassed in the design design1 | Population FIM evaluation | Evaluate the population FIM | Display the population FIM | Covariate tests | Evaluate and display covariate tests | Design optimization | Multiplicative algorithm | Define administration and sampling constraints | Create the constraint arm and the associated design | Set the parameters of the Multiplicative algorithm | Run the Multiplicative algorithm for the optimization with a population FIM | Display and plot Multiplicative algorithm results | Create and save the report for the design optimization | Simplex algorithm | Define sampling constraints | Set the parameters of the Simplex algorithm | Run the Simplex algorithm for the optimization with a population FIM | Display and plot Simplex results | Covariate tests on Simplex optimal design

Overview | Objectives | Experimental design | PK-PD model | Model parameters | Residual error model | Administration, sampling times, arms | Population FIM evaluation | PFIM, PopED, and cross-tool equivalence | General rules (all vignettes) | Variance or SD? One conversion rule | Naming | Tim Waterhouse oral PK (Vignette 04) | What plays the same role | Design and time scale | Structural model implementation | Naming only (no change to the model) | G-CSF Design 1 (this vignette, Vignette 05) | Variance → SD for this model | Comparing RSE with PopED | Relative standard errors | Fixed effects ($\mu$) | Inter-individual variances ($\omega^2$) | Residual error ($\sigma$) | Diagnostic plots | References

Pharmacokinetic models | Compartmental models and parameters | One-compartment models | Models with linear elimination | Intravenous bolus | Infusion | First order absorption | Two-compartment models | First-order absorption | Models with Michaelis-Menten elimination | Pharmacodynamic models | Immediate response models | Drug action models | Baseline/disease models | Turnover response models | Models with impact on the input $(R_{in})$ | Models with impact on the output $(k_{out})$

Introduction | Population model | Structural model | Population parameters | Residual error | Reference body weight | Study design | Evaluation of the initial design | Predicted concentration-time profile | Design summary | D-criterion | Relative standard errors | D-optimal sampling-time optimization | Five samples | Six samples | Discussion