Abstract
Purpose
For nonlinear mixed-effects pharmacometric models, diagnostic approaches often rely on individual parameters, also called empirical Bayes estimates (EBEs), estimated through maximizing conditional distributions. When individual data are sparse, the distribution of EBEs can “shrink” towards the same population value, and as a direct consequence, resulting diagnostics can be misleading.
Methods
Instead of maximizing each individual conditional distribution of individual parameters, we propose to randomly sample them in order to obtain values better spread out over the marginal distribution of individual parameters.
Results
We evaluated, through diagnostic plots and statistical tests, hypothesis related to the distribution of the individual parameters and show that the proposed method leads to more reliable results than using the EBEs. In particular, diagnostic plots are more meaningful, the rate of type I error is correctly controlled and its power increases when the degree of misspecification increases. An application to the warfarin pharmacokinetic data confirms the interest of the approach for practical applications.
Conclusions
The proposed method should be implemented to complement EBEs-based approach for increasing the performance of model diagnosis.
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Abbreviations
- EBE:
-
Empirical Bayes estimates
- MAP:
-
Maximum a posteriori
- MCMC:
-
Markov Chain Monte Carlo
- PD:
-
Pharmacodynamics
- PK:
-
Pharmacokinetics
- PPC:
-
Posterior predictive checks
- VPC:
-
Visual predictive checks
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Supplemental Figure S1
Warfarin PK data. (GIF 30 kb)
Supplemental Figure S2
Empirical distribution of the individual parameters. The estimated pdf’s are displayed in solid line. Top: EBEs, bottom: sampled from the conditional distributions. (GIF 40 kb)
Supplemental Figure S3
Sampled individual parameters versus weight. The correlation coefficient and the p-value of the test r = 0 are displayed for each parameters. (GIF 37 kb)
Supplemental Figure S4
Joint distributions of the sampled random effects. The correlation coefficient and the p-value of the test r = 0 are displayed for each pair of parameters. (GIF 53 kb)
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Lavielle, M., Ribba, B. Enhanced Method for Diagnosing Pharmacometric Models: Random Sampling from Conditional Distributions. Pharm Res 33, 2979–2988 (2016). https://doi.org/10.1007/s11095-016-2020-3
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DOI: https://doi.org/10.1007/s11095-016-2020-3