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Related Experiment Videos

The effect of using a robust optimality criterion in model based adaptive optimization.

Eric A Strömberg1, Andrew C Hooker2

  • 1Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.

Journal of Pharmacokinetics and Pharmacodynamics
|April 8, 2017
PubMed
Summary
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Robust optimality criteria in model-based adaptive optimal design (MBAOD) trials offer greater flexibility. Using the ELD criterion in MBAOD, compared to local criteria, reduces required adaptations for dose optimization studies.

Area of Science:

  • Pharmacometrics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • Model-based adaptive optimal design (MBAOD) offers flexibility over traditional methods.
  • Robust optimality criteria are generally more flexible than local criteria in MBAOD.
  • MBAOD may be less sensitive to initial misspecification of prior information.

Purpose of the Study:

  • To investigate the influence of local (lnD) versus robust (ELD) optimality criteria in MBAOD.
  • To evaluate these criteria for a simulated dose optimization study under rich and sparse sampling.
  • To construct a stopping criterion for MBAOD based on accurate effect prediction.

Main Methods:

  • Simulated dose optimization study using a one-compartment PK model and sigmoidal EMAX PD model.
Keywords:
APIDose optimizationELDModel based adaptive optimal designRobust optimal designStudy design

Related Experiment Videos

  • 50 iterations of MBAOD were performed using the MBAOD R-package.
  • Compared lnD and ELD optimality criteria with misspecified initial parameters, assessing efficiency relative to true lnD-optimal designs.

    • Main Results:

    • MBAOD using the ELD robust optimality criterion converged faster to the true lnD-optimal design.
    • This faster convergence was observed for both rich and sparse sampling schedules.
    • The ELD criterion demonstrated improved efficiency when initial model parameters were misspecified.

    Conclusions:

    • Employing a robust optimality criterion (ELD) in MBAOD can reduce the number of necessary adaptations.
    • This approach enhances the practicality and efficiency of adaptive clinical trials.
    • Robust criteria improve MBAOD's performance, especially when initial model assumptions are inaccurate.