References of "Jullion, Astrid"
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See detailTrial predictions vs. trial simulations in Model-based Drug Development: integrating uncertainties to evaluate the predictive probability of success.
Lebrun, Pierre ULg; Boulanger, Bruno; Jullion, Astrid

Conference (2011, March 02)

In a Model-Based Drug Development strategy, the first objective is to design studies such that the most reliable model estimates are obtained, in order to optimize the design of future studies and to take ... [more ▼]

In a Model-Based Drug Development strategy, the first objective is to design studies such that the most reliable model estimates are obtained, in order to optimize the design of future studies and to take decisions based on predictions. The objectives of the work is to present from a theoretical and practical point of view how to perform trial predictions, as opposed to trial simulations, by integrating the uncertainty of the parameters. The difference between prediction and simulation is important in early development when limited data or prior information are available. Indeed ignoring the uncertainty of parameter estimates can lead to wrong decisions. First, will be provided methodology, derived from Bayesian statistics, to perform trial predictions from the parameter estimates and their uncertainty, when obtained with conventional frequentist population methods. Second, a practical implementation in R will be shown. This generalized prediction shell can cope with any kind of structural population models: Ordinary Differential Equation, single & multiple doses, infusion, etc... The proposed shell is also flexible to allow the testing of various scenarios and study designs, and therefore evaluate the predictive probability of success of different protocols. When joint models for efficacy and safety are established, the Prediction-based Clinical Utility Index (p-CUI) and its distribution can directly be obtained for more riskless decision making. Examples will be shown to highlight in early phases the differences existing between trial prediction and trial simulation. This approach is required to permit Model-Based Drug Development strategy, and impact successfully decision in early clinical phases. [less ▲]

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See detailAdaptive Bayesian P-splines to estimate varying regression coefficients: application to receptor occupancy estimation
Jullion, Astrid; Lambert, Philippe ULg; Vandenhende, François

in JSM Proceedings, Statistical Computing Section. Alexandria, VA: American Statistical Association. (2009)

In many applications of linear regression models, the regression coefficients are not regarded as fixed but as varying with another covariate named the effect modifier. A useful extension of the linear ... [more ▼]

In many applications of linear regression models, the regression coefficients are not regarded as fixed but as varying with another covariate named the effect modifier. A useful extension of the linear regression models are then varying coefficient models. To link the regression coefficient with the effect modifier, several methods may be considered. Here, we propose to use Bayesian P-splines to relate in a smoothed way the regression coefficient with the effect modifier. We show that this method enables a large level of flexibility: if necessary, adaptive penalties can be introduced in the model (Jullion and Lambert 2007) and linear constraints on the relation between the regression coefficient and the effect modifier may easily be added. We provide an illustration of the proposed method in a PET study where we want to estimate the relation between the Receptor Occupancy and the drug concentration in the plasma. As we work in a Bayesian setting, credibility sets are easily obtained for receptor occupancy, which take into account the uncertainty appearing at all the different estimation steps. [less ▲]

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See detailPharmacokinetic parameters estimation using adaptive Bayesian P-splines models
Jullion, Astrid; Lambert, Philippe ULg; Beck, Benoit et al

in Pharmaceutical Statistics (2009), 8

In preclinical and clinical experiments, pharmacokinetic (PK) studies are designed to analyse the evolution of drug concentration in plasma over time i.e. the PK profile. Some PK parameters are estimated ... [more ▼]

In preclinical and clinical experiments, pharmacokinetic (PK) studies are designed to analyse the evolution of drug concentration in plasma over time i.e. the PK profile. Some PK parameters are estimated in order to summarize the complete drug’s kinetic profile: area under the curve (AUC), maximal concentration (Cmax), time at which the maximal concentration occurs (tmax) and half-life time (t1/2). Several methods have been proposed to estimate these PK parameters. A first method relies on interpolating between observed concentrations. The interpolation method is often chosen linear. This method is simple and fast. Another method relies on compartmental modelling. In this case, nonlinear methods are used to estimate parameters of a chosen compartmental model. This method provides generally good results. However, if the data are sparse and noisy, two difficulties can arise with this method. The first one is related to the choice of the suitable compartmental model given the small number of data available in preclinical experiment for instance. Second, nonlinear methods can fail to converge. Much work has been done recently to circumvent these problems (J. Pharmacokinet. Pharmacodyn. 2007; 34:229–249, Stat. Comput., to appear, Biometrical J., to appear, ESAIM P&S 2004; 8:115–131). In this paper, we propose a Bayesian nonparametric model based on P-splines. This method provides good PK parameters estimation, whatever be the number of available observations and the level of noise in the data. Simulations show that the proposed method provides better PK parameters estimations than the interpolation method, both in terms of bias and precision. The Bayesian nonparametric method provides also better AUC and t1/2 estimations than a correctly specified compartmental model, whereas this last method performs better in tmax and Cmax estimations. We extend the basic model to a hierarchical one that treats the case where we have concentrations from different subjects. We are then able to get individual PK parameter estimations. Finally, with Bayesian methods, we can get easily some uncertainty measures by obtaining credibility sets for each PK parameter. [less ▲]

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See detailDesign Space for analytical methods. A Bayesian perspective based on multivariate models and prediction
Lebrun, Pierre ULg; Boulanger, Bruno ULg; Jullion, Astrid et al

Conference (2008, September)

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See detailRobust specification of the roughness penalty prior distribution in spatially adaptive Bayesian P-splines models
Jullion, Astrid; Lambert, Philippe ULg

in Computational Statistics & Data Analysis (2007), 51

The potential important role of the prior distribution of the roughness penalty parameter in the resulting smoothness of Bayesian Psplines models is considered. The recommended specification for that ... [more ▼]

The potential important role of the prior distribution of the roughness penalty parameter in the resulting smoothness of Bayesian Psplines models is considered. The recommended specification for that distribution yields models that can lack flexibility in specific circumstances. In such instances, these are shown to correspond to a frequentist P-splines model with a predefined and severe roughness penalty parameter, an obviously undesirable feature. It is shown that the specification of a hyperprior distribution for one parameter of that prior distribution provides the desired flexibility. Alternatively, a mixture prior can also be used. An extension of these two models by enabling adaptive penalties is provided. The posterior of all the proposed models can be quickly explored using the convenient Gibbs sampler. [less ▲]

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