Evaluation of bootstrapping as a validation technique for population pharmacokinetic models
It has been recommended by the FDA and others that the population pharmacokinetic models (PPKM) need to be validated. This is particularly true when the model plays a key role in the construction of dosing strategies. It was the objective of the current study to evaluate the ability of bootstrapping...
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| Format: | Others |
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Scholarly Commons
2004
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| Online Access: | https://scholarlycommons.pacific.edu/uop_etds/590 https://scholarlycommons.pacific.edu/cgi/viewcontent.cgi?article=1589&context=uop_etds |
| Summary: | It has been recommended by the FDA and others that the population pharmacokinetic models (PPKM) need to be validated. This is particularly true when the model plays a key role in the construction of dosing strategies. It was the objective of the current study to evaluate the ability of bootstrapping to identify PPKMs that were estimated from data without influence observations versus PPKMs from data containing influence observations. The evaluation was performed in four phases. In phase I, ten no-influence index datasets and ten influence index datasets were created. A model parameter (theta !) was estimated for the index datasets. It was found that influence observations caused an over-estimation of the model parameter. In phase II, 200 bootstrap datasets were resampled with replacement from each of the twenty index datasets ( 4000 datasets total).
In phase III, the bootstrapping validation method was executed using NONMEM for model estimation and the resulting statistics were used to detect models developed from influence data. The metrics of choice were mean absolute prediction error (MAPE) and mean squared prediction error (MSPE). In phase IV, the impact of achieving a global minimum in the NONMEM program on the non-parametric bootstrap validation process was investigated. This study showed that the current and widely followed procedure for application of the bootstrap for PPK model validation has significant deficiencies. The achievement of a global minimum in the NONMEM program proved to be an important and pivotal factor when applying bootstrapping to the PPKM validation process. Therefore, we concluded that each bootstrap dataset should be evaluated with several model control streams. Further, the suggested value for an acceptable difference between the NONMEM minimum objective function values for a global and a near global minimum should be 2.5 units. |
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