References of "Vandenhende, François"
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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 detailLocal dependence estimation using semi-parametric Archimedean copulas
Vandenhende, François; Lambert, Philippe ULg

in Canadian Journal of Statistics = Revue Canadienne de Statistique (2005), 33

The authors de¯ne a new semiparametric Archimedean copula family having a °exible depen- dence structure. The family's generator is a local interpolation of existing generators. It has locally-de¯ned ... [more ▼]

The authors de¯ne a new semiparametric Archimedean copula family having a °exible depen- dence structure. The family's generator is a local interpolation of existing generators. It has locally-de¯ned dependence parameters. The authors present a penalized constrained least-squares method to estimate and smooth these parameters. They illustrate the °exibility of their dependence model in a bivariate survival example. [less ▲]

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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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See detailImproved rank-based dependence measures for categorical data
Vandenhende, François; Lambert, Philippe ULg

in Statistics & Probability Letters (2003), 63

We extend rank-based dependence measures like Spearman's rho to categorical data so that the same ±1 limits are always reached under complete dependence. A goodness-of-fit procedure is derived for ... [more ▼]

We extend rank-based dependence measures like Spearman's rho to categorical data so that the same ±1 limits are always reached under complete dependence. A goodness-of-fit procedure is derived for dependence models using copulas. [less ▲]

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See detailA copula based model for multivariate non normal longitudinal data: analysis of a dose titration safety study on a new antidepressant
Lambert, Philippe ULg; Vandenhende, François

in Statistics in Medicine (2002), 21

A new model for multivariate non-normal longitudinal data is proposed. In a first step, each longitudinal series of data corresponding to a given response is modelled separately using a copula to relate ... [more ▼]

A new model for multivariate non-normal longitudinal data is proposed. In a first step, each longitudinal series of data corresponding to a given response is modelled separately using a copula to relate the marginal distributions of the response at each time of observation. In a second step, at each observation time, the conditional (on the past) distributions of each response are related using another copula describing the relationship between the corresponding variables. Note that there is no need to consider the same family of distributions for these response variables. The technique is illustrated in a dose titration safety study on a new antidepressant. The haemodynamic effect on diastolic blood pressure, systolic blood pressure and heart rate is studied. These three responses are measured repeatedly over time on ten healthy volunteers during the dose escalation. The available covariates are sex and the concentration of drug in the plasma at time of measurement. [less ▲]

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See detailOn the joint analysis of longitudinal responses and early discontinuation in randomized trials
Vandenhende, François; Lambert, Philippe ULg

in Journal of Biopharmaceutical Statistics (2002), 12

Our focus is on the joint analysis of longitudinal nonnormal responses and early discontinuation in (pre)-clinical trials. Separate models are fitted to the two series (response and discontinuation) to ... [more ▼]

Our focus is on the joint analysis of longitudinal nonnormal responses and early discontinuation in (pre)-clinical trials. Separate models are fitted to the two series (response and discontinuation) to account for covariate and time effects. The serial dependence and the dependence between response and drop-out are also modeled. This is done using particular dependence functions, called copulas. Copulas are used to create a joint distribution with given marginal distributions. Applications are given for the analysis of heart rate/morbidity in toxicology and pain severity/intake of rescue medications in a trial on migraine. Using copulas, the level of dependence between two variables remains invariant to changes in the marginal distribution of either variable. This proves interesting in modeling the association in a longitudinal setting when responses change over time. [less ▲]

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