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Dive into the research topics where Catherine Timmermans is active.

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Featured researches published by Catherine Timmermans.


Statistics in Medicine | 2014

Linear mixed-effects models for central statistical monitoring of multicenter clinical trials

Lieven Desmet; David Venet; Erik Doffagne; Catherine Timmermans; Tomasz Burzykowski; Catherine Legrand; Marc Buyse

Multicenter studies are widely used to meet accrual targets in clinical trials. Clinical data monitoring is required to ensure the quality and validity of the data gathered across centers. One approach to this end is central statistical monitoring, which aims at detecting atypical patterns in the data by means of statistical methods. In this context, we consider the simple case of a continuous variable, and we propose a detection procedure based on a linear mixed-effects model to detect location differences between each center and all other centers. We describe the performance of the procedure as a function of contamination rate and signal-to-noise ratio. We investigate the effect of center size and variance structure and illustrate the use of the procedure using data from two multicenter clinical trials.


Journal of Multivariate Analysis | 2013

Using Bagidis in nonparametric functional data analysis: Predicting from curves with sharp local features

Catherine Timmermans; Laurent Delsol; Rainer von Sachs

Our goal is to predict a scalar value or a group membership from the discretized observation of curves with sharp local features that might vary both vertically and horizontally. To this aim, we propose to combine the use of the nonparametric functional regression estimator developed by Ferraty and Vieu (2006) [18] with the Bagidis semimetric developed by Timmermans and von Sachs (submitted for publication) [36] with a view of efficiently measuring dissimilarities between curves with sharp patterns. This association is revealed as powerful. Under quite general conditions, we first obtain an asymptotic expansion for the small ball probability indicating that Bagidis induces a fractal topology on the functional space. We then provide the rate of convergence of the nonparametric regression estimator in this case, as a function of the parameters of the Bagidis semimetric. We propose to optimize those parameters using a cross-validation procedure, and show the optimality of the selected vector. This last result has a larger scope and concerns the optimization of any vector parameter characterizing a semimetric used in this context. The performances of our methodology are assessed on simulated and real data examples. Results are shown to be superior to those obtained using competing semimetrics as soon as the variations of the significant sharp patterns in the curves have a horizontal component.


Gastric Cancer | 2016

Statistical monitoring of data quality and consistency in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial

Catherine Timmermans; Erik Doffagne; David Venet; Lieven Desmet; Catherine Legrand; Tomasz Burzykowski; Marc Buyse

IntroductionData quality may impact the outcome of clinical trials; hence, there is a need to implement quality control strategies for the data collected. Traditional approaches to quality control have primarily used source data verification during on-site monitoring visits, but these approaches are hugely expensive as well as ineffective. There is growing interest in central statistical monitoring (CSM) as an effective way to ensure data quality and consistency in multicenter clinical trials.MethodsCSM with SMART™ uses advanced statistical tools that help identify centers with atypical data patterns which might be the sign of an underlying quality issue. This approach was used to assess the quality and consistency of the data collected in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial, involving 1495 patients across 232 centers in Japan.ResultsIn the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial, very few atypical data patterns were found among the participating centers, and none of these patterns were deemed to be related to a quality issue that could significantly affect the outcome of the trial.DiscussionCSM can be used to provide a check of the quality of the data from completed multicenter clinical trials before analysis, publication, and submission of the results to regulatory agencies. It can also form the basis of a risk-based monitoring strategy in ongoing multicenter trials. CSM aims at improving data quality in clinical trials while also reducing monitoring costs.


International Journal of Clinical Oncology | 2016

Data-driven risk identification in phase III clinical trials using central statistical monitoring

Catherine Timmermans; David Venet; Tomasz Burzykowski

Our interest lies in quality control for clinical trials, in the context of risk-based monitoring (RBM). We specifically study the use of central statistical monitoring (CSM) to support RBM. Under an RBM paradigm, we claim that CSM has a key role to play in identifying the “risks to the most critical data elements and processes” that will drive targeted oversight. In order to support this claim, we first see how to characterize the risks that may affect clinical trials. We then discuss how CSM can be understood as a tool for providing a set of data-driven key risk indicators (KRIs), which help to organize adaptive targeted monitoring. Several case studies are provided where issues in a clinical trial have been identified thanks to targeted investigation after the identification of a risk using CSM. Using CSM to build data-driven KRIs helps to identify different kinds of issues in clinical trials. This ability is directly linked with the exhaustiveness of the CSM approach and its flexibility in the definition of the risks that are searched for when identifying the KRIs. In practice, a CSM assessment of the clinical database seems essential to ensure data quality. The atypical data patterns found in some centers and variables are seen as KRIs under a RBM approach. Targeted monitoring or data management queries can be used to confirm whether the KRIs point to an actual issue or not.


Astronomy and Astrophysics | 2014

Dark signal correction for a lukecold frame-transfer CCD - New method and application to the solar imager of the PICARD space mission

Jean-François Hochedez; Catherine Timmermans; Alain Hauchecorne; Mustapha Meftah

Context. Astrophysical observations must be corrected for their imperfections of instrumental origin. When Charge Coupled Devices (CCDs) are used, their dark signal is one such hindrance. In their pristine state, most CCD pixels are ‘cool’, i.e. they exhibit a low, quasi uniform dark current, which can be estimated and corrected for. In space, after having been hit by an energetic particle, pixels can turn ‘hot’, viz. they start delivering excessive, less predictable, dark current. The hot pixels need therefore to be flagged so that subsequent analysis may ignore them. Aims. The image data of the PICARD SODISM solar telescope (Meftah et al. 2013) require dark signal correction and hot pixel identification. Its E2V 42-80 CCD operates at -7.2°C and has a frame transfer architecture. Both image and memory zones thus accumulate dark current during, respectively, integration and readout time. These two components must be separated in order to estimate the dark signal for any observation. This is the main purpose of the Dark Signal Model presented in this paper. Methods. The dark signal time series of every pixel is processed by the ‘unbalanced Haar technique’ (Fryzlewicz 2007) in order to timestamp the instants when its dark signal is expected to change significantly. In-between those, both components are assumed constant, and a robust linear regression vs. integration time provides first estimates and a quality coecient. The latter serves to assign definitive estimates for this pixel and for that period. Results. Our model is part of the SODISM Level 1 data production scheme. To check its reliability, we verify on dark frames that it leaves a negligible residual bias (5e^{-}), and generates a small RMS error (25 e^{-}rms). We also analyze the distribution of the image zone dark current. The cool pixel level is found to be 4.1 e^{-} . pxl^{-1} . s^{-1}, in agreement with the predicted value. The emergence rate of hot pixels is investigated too. It legitimates a threshold criterion at 50 e^{-} . pxl^{-1} . s^{-1}. The growth rate is found to be on average ~500 new hot pixels per day, i.e. 4.2% of the image zone area per year. Conclusions. A new method for dark signal correction of a frame transfer CCD operating at only ca. -10°C is demonstrated. It allows making recommendations about the scientific usage of such CCDs in space. Independently, aspects of the method (adaptation of the unbalanced Haar technique, dedicated robust linear regression) have a generic interest.


Statistics in Biopharmaceutical Research | 2017

Use of the Beta-Binomial Model for Central Statistical Monitoring of Multicenter Clinical Trials

Lieven Desmet; David Venet; Erik Doffagne; Catherine Timmermans; Catherine Legrand; Tomasz Burzykowski; Marc Buyse

ABSTRACT As part of central statistical monitoring of multicenter clinical trial data, we propose a procedure based on the beta-binomial distribution for the detection of centers with atypical values for the probability of some event. The procedure makes no assumptions about the typical event proportion and uses the event counts from all centers to derive a reference model. The procedure is shown through simulations to have high sensitivity and high specificity if the contamination rate is small and the atypical event proportions are the result of some systematic shift in the underlying data-generating mechanism.


Archive | 2011

Bases Giving Distances. A New Semimetric and its Use for Nonparemetric Functional Data Analysis

Catherine Timmermans; Laurent Delsol; Rainer von Sachs

The BAGIDIS semimetric is a highly adaptivewavelet-based semimetric. It is particularly suited for dealing with curves presenting horizontally- and verticallyvarying sharp local patterns. One can advantageously make use of this semimetric in the framework of nonparametric functional data analysis.


Lecture Notes in Statistics | 2015

The BAGIDIS distance: about a fractal topology, with applications to functional classification and prediction

Rainer von Sachs; Catherine Timmermans

The BAGIDIS (semi-) distance of Timmermans and von Sachs (BAGIDIS: statistically investigating curves with sharp local patterns using a new functional measure of dissimilarity. Under revision. http://www.uclouvain.be/en-369695.html. ISBA Discussion Paper 2013-31, Universite catholique de Louvain, 2013) is the central building block of a nonparametric method for comparing curves with sharp local features, with the subsequent goal of classification or prediction. This semi-distance is data-driven and highly adaptive to the curves being studied. Its main originality is its ability to consider simultaneously horizontal and vertical variations of patterns. As such it can handle curves with sharp patterns which are possibly not well-aligned from one curve to another. The distance is based on the signature of the curves in the domain of a generalised wavelet basis, the Unbalanced Haar basis. In this note we give insights on the problem of stability of our proposed algorithm, in the presence of observational noise. For this we use theoretical investigations from Timmermans, Delsol and von Sachs (J Multivar Anal 115:421–444, 2013) on properties of the fractal topology behind our distance-based method. Our results are general enough to be applicable to any method using a distance which relies on a fractal topology.


12th European Symposium on Statistical Methods for the Food Industry | 2012

Advantages of the Bagidis methodology for metabonomics analyses: application to a spectroscopic study of Age-related Macular Degeneration

Catherine Timmermans; Pascal De Tullio; Vincent Lambert; Michel Frederich; Réjane Rousseau; Rainer von Sachs


Archive | 2010

BAGIDIS, a new method for statistical analysis of differences between curves with sharp discontinuities

Catherine Timmermans; Rainer von Sachs

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Rainer von Sachs

Université catholique de Louvain

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David Venet

Université libre de Bruxelles

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Catherine Legrand

Université catholique de Louvain

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Erik Doffagne

Université catholique de Louvain

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Lieven Desmet

Université catholique de Louvain

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