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Predictive performance of internal and external validation procedures
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
(English)Article in journal (Other academic) Submitted
Abstract [en]

Purpose: To compare estimates of predictive performance between internal (IV) and external data-splitting (EV) validation procedures. Methods: Datasets of different study size (n=6, 12, 24, 48, 96, 192, or 384 individuals) were simulated from a one compartment, first-order absorption, pharmacokinetic model and both parametric (FOCE), and nonparametric (NONP) parameter estimates were obtained in NONMEM. From these, three different validation procedures (IV, EV, and a population validation (PV)) were undertaken by means of numerical predictive checks (NPCs) to provide estimates of predictive performance, the PV procedure serving as a reference to assess performance of IV and EV. The predictive performance of NONP versus FOCE estimates was further assessed. Results: Estimates of predictive performance for predicting the median of the population distribution had in general significantly lower imprecision for IV than EV, with little bias for both procedures. For small study sizes, n=6-12 (FOCE) or n=6-24 (NONP), the tails of the population distribution were significantly more biased with IV than EV, but similar imprecision was obtained. The predictive performance for FOCE was similar or superior to that of NONP. Conclusions: Data-splitting is inferior to IV when evaluating predictive models to retain sufficient precision both in predictions and in estimates of predictive performance.

Identifiers
URN: urn:nbn:se:uu:diva-132776OAI: oai:DiVA.org:uu-132776DiVA: diva2:360105
Available from: 2010-11-02 Created: 2010-10-26 Last updated: 2011-04-11Bibliographically approved
In thesis
1. Benefits of Pharmacometric Model-Based Design and Analysis of Clinical Trials
Open this publication in new window or tab >>Benefits of Pharmacometric Model-Based Design and Analysis of Clinical Trials
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Quantitative pharmacokinetic-pharmacodynamic and disease progression models are the core of the science of pharmacometrics which has been identified as one of the strategies that can make drug development more effective. To adequately develop and utilize these models one needs to carefully consider the nature of the data, choice of appropriate estimation methods, model evaluation strategies, and, most importantly, the intended use of the model.

The general aim of this thesis was to investigate how the use of pharmacometric models can improve the design and analysis of clinical trials within drug development. The development of pharmacometric models for clinical assessment scales in stroke and graded severity events, in this thesis, show the benefit of describing data as close to its true nature as possible, as it increases the predictive abilities and allows for mechanistic interpretations of the models. Performance of three estimation methods implemented in the mixed-effects modeling software NONMEM; 1) Laplace, 2) SAEM, and 3) Importance sampling, applied when modeling repeated time-to-event data, was investigated. The two latter methods are to be preferred if less than approximately half of the individuals experience events. In addition, predictive performance of two validation procedures, internal and external validation, was explored, with internal validation being preferred in most cases. Model-based analysis was compared to conventional methods by the use of clinical trial simulations and the power to detect a drug effect was improved with a pharmacometric design and analysis.

Throughout this thesis several examples have shown the possibility of significantly reducing sample sizes in clinical trials with a pharmacometric model-based analysis. This approach will reduce time and costs spent in the development of new drug therapies, but foremost reduce the number of healthy volunteers and patients exposed to experimental drugs.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2010. 71 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 133
Keyword
model-based analysis, pharmacometrics, modeling, disease progression, NONMEM, SAEM, Importance sampling, repeated time-to-event, RTTCE, RCEpT, NIH stroke scale, Barthel index, internal validation, external validation, study power, study design
National Category
Pharmaceutical Sciences
Research subject
Pharmacokinetics and Drug Therapy
Identifiers
urn:nbn:se:uu:diva-133104 (URN)
Public defence
2010-12-17, B41, Biomedicinskt Centrum, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2010-11-24 Created: 2010-11-02 Last updated: 2011-01-13Bibliographically approved
2. 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.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 136
Keyword
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
Identifiers
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)
Opponent
Supervisors
Available from: 2011-03-09 Created: 2011-01-31 Last updated: 2011-05-04Bibliographically approved

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Baverel, Paul GKarlsson, Kristin EKarlsson, Mats O

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