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A case study of modeling and exposure-response prediction for count data.
Hui Quan1, Xuezhou Mao, Lynn Wei
1a Biostatistics and Programming, Sanofi, Bridgewater , New Jersey , USA.
Journal of Biopharmaceutical Statistics
|June 11, 2014
Summary
Determining the optimal drug dose can be challenging. Modeling plasma concentration data helps predict treatment effects for unstudied doses, aiding final dose selection in clinical trials.
Area of Science:
- Pharmacometrics
- Clinical Trial Design
- Biostatistics
Background:
- Selecting the final commercial drug dose can be difficult even after Phase III studies.
- Traditional methods may not fully leverage available plasma concentration data for dose optimization.
Purpose of the Study:
- To illustrate a modeling approach for predicting treatment effects at various plasma concentrations.
- To demonstrate how this approach can justify final dose selection in Phase III clinical trials.
Main Methods:
- Utilizing plasma concentration data from Phase III studies.
- Applying pharmacokinetic-pharmacodynamic (PK/PD) modeling to link concentration and effect.
- Comparing statistical models including overdispersed Poisson, negative binomial, and recurrent event models.
Main Results:
- The negative binomial model demonstrated superior data fitting.
- The chosen model allowed for within-treatment and between-treatment comparisons.
- The modeling approach successfully predicted treatment effects for unstudied doses.
Conclusions:
- Modeling plasma concentration data is a valuable tool for dose confirmation.
- The negative binomial model is recommended for count data in similar scenarios.
- This method supports evidence-based final dose selection in drug development.
Keywords:
Annualized relapse rateNegative binomialPlasma concentrationRecurrent eventRisk ratioTrial simulationMore Related Videos
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