1,584 research outputs found
Designing a paediatric study for an antimalarial drug including prior information from adults
International audienceThe objectives of this study were to design a pharmacokinetic (PK) study by using information about adults and evaluate the robustness of the recommended design through a case study of mefloquine. PK data about adults and children were available from two different randomized studies of the treatment of malaria with the same artesunate-mefloquine combination regimen. A recommended design for pediatric studies of mefloquine was optimized on the basis of an extrapolated model built from adult data through the following approach. (i) An adult PK model was built, and parameters were estimated by using the stochastic approximation expectation-maximization algorithm. (ii) Pediatric PK parameters were then obtained by adding allometry and maturation to the adult model. (iii) A D-optimal design for children was obtained with PFIM by assuming the extrapolated design. Finally, the robustness of the recommended design was evaluated in terms of the relative bias and relative standard errors (RSE) of the parameters in a simulation study with four different models and was compared to the empirical design used for the pediatric study. Combining PK modeling, extrapolation, and design optimization led to a design for children with five sampling times. PK parameters were well estimated by this design with few RSE. Although the extrapolated model did not predict the observed mefloquine concentrations in children very accurately, it allowed precise and unbiased estimates across various model assumptions, contrary to the empirical design. Using information from adult studies combined with allometry and maturation can help provide robust designs for pediatric studies
Mixed effects modelling for biological systems
En raison des relations complexes entre les variables des systèmes biologiques, l’hétérogénéité
des données biologiques pose un défi pour leur modélisation par des modèles mathématiques
et statistiques. En réponse, étant conçus pour traiter des données multiniveaux et bruitées, les
modèles à effets mixtes deviennent de plus en plus populaires en modélisation quantitative de
systèmes biologiques. L'objectif de cette thèse est de présenter l’application de modèles à effets
mixtes à différents systèmes biologiques.
Le deuxième chapitre de ce mémoire vise à déterminer la relation entre la cote de qualité du
sirop d'érable, divers indicateurs de qualité couramment obtenus par les producteurs ainsi qu'un
nouvel indicateur, le COLORI, et la concentration en acides aminés (AA). Pour cela, nous avons
créé deux modèles à effets mixtes : le premier est un modèle ordinal qui prédit directement la
cote de qualité du sirop d'érable en utilisant la transmittance, COLORI et AA ; le deuxième modèle
est un modèle non linéaire qui prédit la concentration en AA en utilisant COLORI avec le pH
comme approximation temporelle. Nos résultats montrent que la concentration en AA est un
bon prédicteur de la qualité du sirop d'érable et que COLORI est un bon prédicteur de la
concentration en AA.
Le troisième chapitre traite de l’utilisation d’un modèle de la pharmacocinétique de population
(PopPK) pour décrire la dynamique de l'estradiol dans un modèle de pharmacologie quantitative
des systèmes (QSP) de la différenciation des cellules mammaires en cellules myoépithéliales afin
de capturer l'hétérogénéité de la population de patients. Nous avons trouvé que la composante
PopPK du modèle QSP n’a pas ajoutée de grande variation dans la dynamique de patients virtuels,
ce qui suggère que le modèle QSP inclut intrinsèquement l'hétérogénéité.
Dans l'ensemble, ce mémoire démontre l'application de modèles à effets mixtes au systèmes
biologiques pour comprendre l'hétérogénéité des données biologiques.Modelling biological systems with mathematical models has been a challenge due to the
tendency for biological data to be heavily heterogeneous with complex relationships between
the variables. Mixed effects models are an increasingly popular choice as a statistical model for
biological systems since it is designed for multilevel data and noisy data. The aim of this thesis is
to showcase the range of usage of mixed effects modelling for different biological systems.
The second chapter aims to determine the relationship between maple syrup quality rating and
various quality indicator commonly obtained by producers as well as a new indicator, COLORI,
and amino acid (AA) concentration. For this, we created two mixed effects models: the first is an
ordinal model that directly predicts maple syrup quality rating using transmittance, COLORI and
AA; the second model is a nonlinear model that predicts AA concentration using COLORI with pH
as a time proxy. Our models show that AA concentration is a good predictor for maple syrup
quality, and COLORI is a good predictor for AA concentration.
The third chapter involves using a population pharmacokinetics (PopPK) model to estimate
estradiol dynamics in a quantitative systems pharmacokinetics (QSP) model for mammary cell
differentiation into myoepithelial cells in order to capture population heterogeneity among
patients. Our results show that the QSP model inherently includes heterogeneity in its structure
since the added PopPK estradiol portion of the model does not add large variation in the
estimated virtual patients.
Overall, this thesis demonstrates the application of mixed effects models in biology as a way to
understand heterogeneity in biological data
Optimal Experimental Design for Model Selection: a Partial Review
Model selection is a core topic in modern Statistics. This is a review of what has been researched on optimal experimental design for model selection. The aim is to find good designs for increasing the test power for discriminating between rival models. This topic has a special impact nowadays in the area of experimental design
Recent advances in methodology for clinical trials in small populations : the InSPiRe project
Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017.
The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods
Dosing Optimization of Beta-Lactam Antibiotics using Parametric and Nonparametric Population Pharmacokinetic Models
This thesis is mainly focused on dosing evaluation of old antibiotics using parametric and nonparametric population pharmacokinetic models. Additionally, modelling-related challenges during dosing evaluation were identified and recommendations on the use of these models during drug development and in clinical practise were set up
Optimal Study Designs for Cluster Randomised Trials: An Overview of Methods and Results
There are multiple cluster randomised trial designs that vary in when the
clusters cross between control and intervention states, when observations are
made within clusters, and how many observations are made at that time point.
Identifying the most efficient study design is complex though, owing to the
correlation between observations within clusters and over time. In this
article, we present a review of statistical and computational methods for
identifying optimal cluster randomised trial designs. We also adapt methods
from the experimental design literature for experimental designs with
correlated observations to the cluster trial context. We identify three broad
classes of methods: using exact formulae for the treatment effect estimator
variance for specific models to derive algorithms or weights for cluster
sequences; generalised methods for estimating weights for experimental units;
and, combinatorial optimisation algorithms to select an optimal subset of
experimental units. We also discuss methods for rounding weights to whole
numbers of clusters and extensions to non-Gaussian models. We present results
from multiple cluster trial examples that compare the different methods,
including problems involving determining optimal allocation of clusters across
a set of cluster sequences, and selecting the optimal number of single
observations to make in each cluster-period for both Gaussian and non-Gaussian
models, and including exchangeable and exponential decay covariance structures
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