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A novel covariate search method intended for PKPD models with nonparametric parameter distributions
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Objective. To develop a new covariate modeling approach adapted for nonparametric parameter distributions and to evaluate its statistical properties in terms of power and type-I error rate of covariate inclusion.

Methods. The proposed methodology is articulated around the decomposition of the nonparametric joint density obtained in NONMEM into a set of unique individual probability density distributions. These individual probabilities are then exported into R and used as weighting factors of a generalized additive model (GAM) regressing support points on covariate distributions. A calibration of the method is undertaken by means of 1000 randomization tests automated with GAM analyses to derive a decision criterion based on the Akaike’s information criterion (AIC) given the null hypothesis and a user-defined confidence level α. Statistical properties of the proposed methodology were then evaluated through Monte-Carlo simulations with α=5%. Eight scenarios of 1000 stochastic simulations followed by estimations (SSEs) were performed under FOCE-NONP given a 1-compartment pharmacokinetic model and an informative design. Estimates of the statistical power of inclusion of both a continuous and a categorical covariates with varying correlation strengths on CL were obtained with associated estimates of type-I error rate. A comparison was then intended with likelihood ratio test statistics (LRTs) given FOCE parameter distributions. Errors in estimates of correlation coefficients were further assessed.

Results. The methodology was successfully implemented by means of a Perl script calling PsN, NONMEM and R. Estimates of statistical power and type-I error rate of the proposed method were in close agreement with LRT statistics under ideal conditions of hypothesis-testing for the latter, and this, regardless of the correlation strengths and of the attributes of the covariate distribution investigated. Estimates of regression coefficients presented negligible bias and were as precise as the ones obtained with parametric models.

Conclusions. The set of covariate analysis tools is extended with a new, calibrated, covariate identification technique intended for nonparametric population models.

URN: urn:nbn:se:uu:diva-144098OAI: oai:DiVA.org:uu-144098DiVA: diva2:392454
Available from: 2011-01-27 Created: 2011-01-27 Last updated: 2013-09-04
In thesis
1. Development and Evaluation of Nonparametric Mixed Effects Models
Open this publication in new window or tab >>Development and Evaluation of Nonparametric Mixed Effects Models
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A nonparametric population approach is now accessible to a more comprehensive network of modelers given its recent implementation into the popular NONMEM application, previously limited in scope by standard parametric approaches for the analysis of pharmacokinetic and pharmacodynamic data.

The aim of this thesis was to assess the relative merits and downsides of nonparametric models in a nonlinear mixed effects framework in comparison with a set of parametric models developed in NONMEM based on real datasets and when applied to simple experimental settings, and to develop new diagnostic tools adapted to nonparametric models.

Nonparametric models as implemented in NONMEM VI showed better overall simulation properties and predictive performance than standard parametric models, with significantly less bias and imprecision in outcomes of numerical predictive check (NPC) from 25 real data designs. This evaluation was carried on by a simulation study comparing the relative predictive performance of nonparametric and parametric models across three different validation procedures assessed by NPC. The usefulness of a nonparametric estimation step in diagnosing distributional assumption of parameters was then demonstrated through the development and the application of two bootstrapping techniques aiming to estimate imprecision of nonparametric parameter distributions. Finally, a novel covariate modeling approach intended for nonparametric models was developed with good statistical properties for identification of predictive covariates.

In conclusion, by relaxing the classical normality assumption in the distribution of model parameters and given the set of diagnostic tools developed, the nonparametric approach in NONMEM constitutes an attractive alternative to the routinely used parametric approach and an improvement for efficient data analysis.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2011. 68 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 136
nonparametric, model, pharmacometrics, pharmacokinetics, pharmacodynamic, imprecision, covariate analysis, parameter distribution
National Category
Computer and Information Science Pharmacology and Toxicology
Research subject
Biopharmaceutics; Clinical Pharmacology; Computer Systems; Pharmacology; Statistics
urn:nbn:se:uu:diva-144583 (URN)978-91-554-7995-4 (ISBN)
Public defence
2011-03-22, Room B21, BMC, Husarg. 3, Uppsala, 09:15 (English)
Available from: 2011-03-09 Created: 2011-01-31 Last updated: 2011-05-04Bibliographically approved

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