Accelerating Monte Carlo power studies through parametric power estimation

Estimating the power for a non-linear mixed-effects model-based analysis is challenging due to the lack of a closed form analytic expression. Often, computationally intensive Monte Carlo studies need to be employed to evaluate the power of a planned experiment. This is especially time consuming if full power versus sample size curves are to be obtained. A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte Carlo simulation and estimations. The estimated parameter linearly scales with study size allowing a quick generation of the full power versus study size curve. A comparison of the PPE with the classical, purely Monte Carlo-based power estimation (MCPE) algorithm for five diverse pharmacometric models showed an excellent agreement between both algorithms, with a low bias of less than 1.2 % and higher precision for the PPE. The power extrapolated from a specific study size was in a very good agreement with power curves obtained with the MCPE algorithm. PPE represents a promising approach to accelerate the power calculation for non-linear mixed effect models

Pourquoi les politiques publiques sont-elles si peu suivies d’effets ?:Quelques interrogations

L’insertion des femmes sur le marché du travail a connu à la fois des avancées et des reculs. Si davantage de femmes accèdent à l’éducation supérieure et aux emplois qualifiés, d’autres sont touchées par la précarité et connaissent une dégradation de leurs conditions de travail et de vie. Face à ce constat ambivalent, on peut questionner la mise en œuvre et l’efficacité des politiques qui visent à promouvoir l’égalité entre les femmes et les hommes. Cet article a pour objectif de soulever quelques débats. Le plus souvent, les politiques publiques au sens large (y compris la protection sociale) sont définies en termes de compensation et de correction des inégalités et des discriminations. Mais elles ne concernent pas les causes effectives de l’extension du sous-emploi des femmes, qui relèvent du fonctionnement même du marché du travail. C’est donc la définition des politiques publiques qu’il faut interroger, en dépassant une vision binaire qui oppose d’une part un champ économique extérieur, d’autre part un champ social, juridique et culturel qui, seul, pourrait être l’objet d’inflexions. En réalité, le champ économique est aussi le produit des politiques publiques : la libre-concurrence et la prééminence du marché sont le résultat d’une action volontaire des États. Il faut donc réintégrer les politiques économiques dans le champ de la réflexion sur les moyens de combattre les discriminations à l’encontre des femmes.The integration of women into the labour market has gone through both upswings and downturns. In view of this ambivalent result, we can question the efficiency of public policies set up to overcome gender inequality and fight gender discrimination. Does a real will exist, and if so why is it so inefficient or so poorly implemented? What forms do individual and collective resistance take? Most of the time, public policies are defined in terms of compensation and correction. But they don’t deal with the actual causes of women’s underemployment resulting from labour market adjustments. It is therefore the definition of the public policies that we need to examine, going beyond a binary view that opposes economic issues, on the one hand, to social, juridical and cultural concerns on the other

Deutschland auf dem Weg zur Klimaneutralität 2045 - Szenarien und Pfade im Modellvergleich (Zusammenfassung)

Erstmals stellt die vorliegende Szenarienanalyse für Deutschland konkreteTransformationspfade zur Klimaneutralität 2045 auf der Basis eines umfassenden Modellvergleichs vor. Das Besondere an dieser Studie des Ariadne-Projektes ist, dass sechs Gesamtsystem- und Sek-tormodelle in einer Studie integriert wurden, die sich in ihren jeweiligen Stärken ergänzen: Für spezifische Fragestellungen wurde jeweils dasjenige Modell als Leitmodell hervorgehoben, welches die entsprechenden Aspekte am genauesten abbildet. Weitere Modelle wurden genutzt, um Auswirkungen der Transformation auf Umweltschutzgüter und die Verteilung der Kosten auf verschiedene Einkommensgruppen zu analysieren.Dieser breit gefächerte Ansatz ermöglicht es, die Implikationen der Energiewende robust und im Detail zu beschreiben

Novel Pharmacometric Methods for Design and Analysis of Disease Progression Studies

With societies aging all around the world, the global burden of degenerative diseases is expected to increase exponentially. From the perspective drug development, degenerative diseases represent an especially challenging class. Clinical trials, in this context often termed disease progression studies, are long, costly, require many individuals, and have low success rates. Therefore, it is crucial to use informative study designs and to analyze efficiently the obtained trial data. The development of novel approaches intended towards facilitating both the design and the analysis of disease progression studies was the aim of this thesis. This aim was pursued in three stages (i) the characterization and extension of pharmacometric software, (ii) the development of new methodology around statistical power, and (iii) the demonstration of application benefits. The optimal design software PopED was extended to simplify the application of optimal design methodology when planning a disease progression study. The performance of non-linear mixed effect estimation algorithms for trial data analysis was evaluated in terms of bias, precision, robustness with respect to initial estimates, and runtime. A novel statistic allowing for explicit optimization of study design for statistical power was derived and found to perform superior to existing methods. Monte-Carlo power studies were accelerated through application of parametric power estimation, delivering full power versus sample size curves from a few hundred Monte-Carlo samples. Optimal design and an explicit optimization for statistical power were applied to the planning of a study in Alzheimer's disease, resulting in a 30% smaller study size when targeting 80% power. The analysis of ADAS-cog score data was improved through application of item response theory, yielding a more exact description of the assessment score, an increased statistical power and an enhanced insight in the assessment properties. In conclusion, this thesis presents novel pharmacometric methods that can help addressing the challenges of designing and planning disease progression studies

Modeling Composite Assessment Data Using Item Response Theory

Composite assessments aim to combine different aspects of a disease in a single score and are utilized in a variety of therapeutic areas. The data arising from these evaluations are inherently discrete with distinct statistical properties. This tutorial presents the framework of the item response theory (IRT) for the analysis of this data type in a pharmacometric context. The article considers both conceptual (terms and assumptions) and practical questions (modeling software, data requirements, and model building)

Novel Pharmacometric Methods for Design and Analysis of Disease Progression Studies

With societies aging all around the world, the global burden of degenerative diseases is expected to increase exponentially. From the perspective drug development, degenerative diseases represent an especially challenging class. Clinical trials, in this context often termed disease progression studies, are long, costly, require many individuals, and have low success rates. Therefore, it is crucial to use informative study designs and to analyze efficiently the obtained trial data. The development of novel approaches intended towards facilitating both the design and the analysis of disease progression studies was the aim of this thesis. This aim was pursued in three stages (i) the characterization and extension of pharmacometric software, (ii) the development of new methodology around statistical power, and (iii) the demonstration of application benefits. The optimal design software PopED was extended to simplify the application of optimal design methodology when planning a disease progression study. The performance of non-linear mixed effect estimation algorithms for trial data analysis was evaluated in terms of bias, precision, robustness with respect to initial estimates, and runtime. A novel statistic allowing for explicit optimization of study design for statistical power was derived and found to perform superior to existing methods. Monte-Carlo power studies were accelerated through application of parametric power estimation, delivering full power versus sample size curves from a few hundred Monte-Carlo samples. Optimal design and an explicit optimization for statistical power were applied to the planning of a study in Alzheimer's disease, resulting in a 30% smaller study size when targeting 80% power. The analysis of ADAS-cog score data was improved through application of item response theory, yielding a more exact description of the assessment score, an increased statistical power and an enhanced insight in the assessment properties. In conclusion, this thesis presents novel pharmacometric methods that can help addressing the challenges of designing and planning disease progression studies

Improved numerical stability for the bounded integer model

This article highlights some numerical challenges when implementing the bounded integer model for composite score modeling and suggests an improved implementation. The improvement is based on an approximation of the logarithm of the error function. After presenting the derivation of the improved implementation, the article compares the performance of the algorithm to a naive implementation of the log-likelihood using both simulations and a real data example. In the simulation setting, the improved algorithm yielded more precise and less biased parameter estimates when the within-subject variability was small and estimation was performed using the Laplace algorithm. The estimation results did not differ between implementations when the SAEM algorithm was used. For the real data example, bootstrap results differed between implementations with the improved implementation producing identical or better objective function values. Based on the findings in this article, the improved implementation is suggested as the new default log-likelihood implementation for the bounded integer model

A new method for evaluation of the Fisher information matrix for discrete mixed effect models using Monte Carlo sampling and adaptive Gaussian quadrature

International audienceThe design of experiments for discrete mixed effect models is challenging due to the unavailability of a closed-form expression for the Fisher information matrix (FIM), on which most optimality criteria depend. Existing approaches for the computation of the FIM for those models are all based on approximations of the likelihood. A new method is presented which is based on derivatives of the exact conditional likelihood and which uses Monte Carlo (MC) simulations as well as adaptive Gaussian quadrature (AGQ) to integrate those derivatives over the data and random effects. The method is implemented in R and evaluated with respect to the influence of the tuning parameter, the accuracy of the FIM approximation, and computational complexity. The accuracy evaluation is performed by comparing the expected relative standard errors (RSE) from the MC/AGQ FIM with RSE obtained in a simulation study with four different discrete data models (two binary, one count and one repeated time-to-event model) and three different estimation algorithms. Additionally, the results from the MC/AGQ FIM are compared with expected RSE from a marginal quasi-likelihood (MQL) approximated FIM. The comparison resulted in close agreement between the MC/AGQ-based RSE and empirical RSE for all models investigated, and better performance of MC/AGQ than the MQL approximated FIM for variance parameters. The MC/AGQ method also proved to be well suited to calculate the expected power to detect a group effect for a model with binary outcomes