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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 detailAn equivalence test for comparing DNA sequences
Van Steen, Kristel ULg; Raby, B.; Molenberghs, G. et al

in Pharmaceutical Statistics (2005), 4(3), 203-214

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See detailStatistical models for the analysis of controlled trials on acute migraine
Vandenhende, François; Lambert, Philippe ULg

in Pharmaceutical Statistics (2003), 2

pecific efficacy criteria were defined by the International Headache Society for controlled clinical trials on acute migraine. They are derived from the pain profile and the timing of rescue medication ... [more ▼]

pecific efficacy criteria were defined by the International Headache Society for controlled clinical trials on acute migraine. They are derived from the pain profile and the timing of rescue medication intake. We present a methodology to improve the analysis of such trials. Instead of analysing each endpoint separately, we model the joint distribution and derive success rates in any criteria as predictions. We use cumulative regression models for each response at a time and a multivariate normal copula to model the dependence between responses. Parameters are estimated using maximum likelihood. Benefits of the method include a reduction in the number of tests performed and an increase in their power. The method is well suited to dose-response trials from which predictions can be used to select doses and optimize the design of subsequent trials. More generally, our method permits a very flexible modelling of longitudinal series of ordinal data. [less ▲]

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