References of "Timmermans, Catherine"
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See detailUse of the Beta-Binomial Model for Central Statistical Monitoring of Multicenter Clinical Trials
Desmet, L.; Venet, D.; Doffagne, E. et al

in Statistics in Biopharmaceutical Research (2017), 9(1), 1-11

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 ... [more ▼]

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. © 2017 American Statistical Association. [less ▲]

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See detailData-driven risk identification in phase III clinical trials using central statistical monitoring
Timmermans, Catherine ULiege; Venet, David; Burzykowski, Tomasz

in International Journal of Clinical Oncology (2016), 21

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 ... [more ▼]

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. [less ▲]

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See detailStatistical monitoring of data quality and consistency in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial
Timmermans, Catherine ULiege; Doffagne, Erik; Venet, David et al

in Gastric Cancer (2016), 19(1), 24-30

Data 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 ... [more ▼]

Data 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. [less ▲]

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See detailNo behavioural response to kin competition in a lekking species
Lebigre, C.; Timmermans, Catherine ULiege; Soulsbury, C. D.

in Behavioral Ecology and Sociobiology (2016), 70(9), 1457-1465

Abstract: The processes of kin selection and competition may occur simultaneously if limited individual dispersal, i.e. population viscosity, is the only cause of the interactions between kin. Therefore ... [more ▼]

Abstract: The processes of kin selection and competition may occur simultaneously if limited individual dispersal, i.e. population viscosity, is the only cause of the interactions between kin. Therefore, the net indirect benefits of a specific behaviour may largely depend on the existence of mechanisms dampening the fitness costs of competing with kin. Because of female preference for large aggregations, males in lekking species may gain indirect fitness benefits by displaying with close relatives. At the same time, kin selection may also lead to the evolution of mechanisms that dampen the costs of kin competition. As this mechanism has largely been ignored to date, we used detailed behavioural and genetic data collected in the black grouse Lyrurus tetrix to test whether males mitigate the costs of kin competition through the modulation of their fighting behaviours according to kinship and the avoidance of close relatives when establishing a lek territory. We found that neighbouring males’ fighting behaviour was unrelated to kinship and males did not avoid settling with close relatives on leks. As males’ current and future mating success are strongly related to their behaviour on the lek (including fighting behaviour and territory position), the costs of kin competition may be negligible relative to the direct benefits of successful male-male contests. As we previously showed that the indirect fitness benefits of group membership were very limited in this black grouse population, these behavioural data support the idea that direct fitness benefits gained by successful male-male encounters likely outbalance any indirect fitness benefits. Significance statement: Kin selection might be involved in the formation of groups because the fitness benefits of increasing group size can be accrued when groups hold close relatives. However, the fitness costs of competing with kin could counter-balance these indirect fitness benefits unless mechanisms enabling individuals to limit kin competition. Here we show in a lekking species that males do not modulate their fight frequency and intensity according to their kinship and do not avoid establishing territories with closely related neighbours. As the indirect fitness benefits of group display were very small in this system and as this study shows that males do not show any sign of kin competition avoidance, the indirect effects associated with male group display are likely to be very small. © 2016, Springer-Verlag Berlin Heidelberg. [less ▲]

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See detailThe BAGIDIS distance: about a fractal topology, with applications to functional classification and prediction
von Sachs, Rainer; Timmermans, Catherine ULiege

in Antoniadis, Anestis; Poggi, Jean-Michel; Brossat, Xavier (Eds.) Modeling and Stochastic Learning for Forecasting in High Dimensions (2015)

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 ... [more ▼]

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, Université 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 (JMultivar 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. [less ▲]

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See detailSHAH: SHape-Adaptive Haar wavelets for image processing
Fryzlewicz, Piotr; Timmermans, Catherine ULiege

in Journal of Computational and Graphical Statistics (2015), 25(3), 879-898

We propose the SHAH (SHape-Adaptive Haar) transform for images, which results in an orthonormal, adaptive decomposition of the image into Haar-wavelet-like components, arranged hierarchically according to ... [more ▼]

We propose the SHAH (SHape-Adaptive Haar) transform for images, which results in an orthonormal, adaptive decomposition of the image into Haar-wavelet-like components, arranged hierarchically according to decreasing importance, whose shapes reflect the features present in the image. The decomposition is as sparse as it can be for piecewise-constant images. It is performed via an stepwise bottom-up algorithm with quadratic computational complexity; however, nearly-linear variants also exist. SHAH is rapidly invertible. We show how to use SHAH for image denoising. Having performed the SHAH transform, the coefficients are hard- or soft-thresholded, and the inverse transform taken. The SHAH image denoising algorithm compares favourably to the state of the art for piecewise-constant images. A clear asset of the methodology is its very general scope: it can be used with any images or more generally with any data that can be represented as graphs or networks. [less ▲]

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See detailA novel semi-distance for measuring dissimilarities of curves with sharp local patterns
Timmermans, Catherine ULiege; von Sachs, Rainer

in Journal of Statistical Planning and Inference (2015), 160

With this article, we define, investigate and exploit an efficient measure of the dissimilarity between curves that show sharp local features. Examples for such data typically arise in numerous scientific ... [more ▼]

With this article, we define, investigate and exploit an efficient measure of the dissimilarity between curves that show sharp local features. Examples for such data typically arise in numerous scientific fields, including medicine (e.g. H-NMR spectroscopic data for metabonomic analyses, EEG or ECG spectral analysis), geophysics (e.g. earth quake data) or astronomy (e.g. solar irradiance time series). A given peak in a set of such curves might be affected, from one curve to the other, by a vertical amplification, a horizontal shift or both simultaneously. Then, in the presence of horizontal shifts, commonly used dissimilarity measures do not return coherent results when comparing a large number of these curves, for instance for subsequent functional classification or prediction purposes. In this work we propose therefore a new dissimilarity measure which has the ability to capture both horizontal and vertical variations of the peaks, in a unified framework, i.e. in a coherent way within an integrated procedure (avoiding any preprocessing, e.g. in case of misalignment). This dissimilarity measure is embedded within a complete algorithmic procedure, which we call the Bagidis methodology, and which as such is our new proposal for investigating datasets of curves with sharp local features. We strongly suggest to use it replacing classical distances, such as the Euclidean distance between the values (vertical amplitudes) of the observed curve data, in any distance based statistical tool aimed at analyzing datasets with curves having sharp local patterns. Along some typical examples of curve comparison, e.g. in the context of classification or prediction, we show in particular how the use of the Bagidis distance improves the statistical analysis in many situations without being harmful for cases when not giving any advantage over the Euclidean distance (i.e. in the absence of horizontally shifted sharp local patterns). As a key ingredient of our approach we note that it is based upon the expansion of each curve in a different (orthogonal) wavelet basis, one that is particularly suited to the curve. In order to define the Bagidis (semi-) distance, we do not only take into account the differences between the projections of the series onto the bases, as usual, but also the differences between the bases. Therefore, the name Bagidis chosen for the method stands for BAses GIving DIStances. [less ▲]

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See detailLinear mixed-effects models for central statistical monitoring of multicenter clinical trials.
Desmet, Lieven; Venet, D.; Doffagne, E. et al

in Statistics in Medicine (2014), 33(30), 5265-5279

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 ... [more ▼]

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. Copyright © 2014 John Wiley & Sons, Ltd. [less ▲]

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See detailDark signal correction for a lukecold frame-transfer CCD: New method and application to the solar imager of the PICARD space mission
Hochedez, J.-F.; Timmermans, Catherine ULiege; Hauchecorne, A. et al

in Astronomy and Astrophysics (2014), 561(A17), 1-16

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 ... [more ▼]

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, that is, 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 therefore need to be flagged so that a subsequent analysis may ignore them. Aims. The image data of the PICARD-SODISM solar telescope 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 integration and readout time, respectively. These two components must be separated in order to estimate the dark signal for any given observation. This is the main purpose of the dark signal model presented in this paper. Methods. The dark signal time-series of every pixel was processed by the unbalanced Haar technique to timestamp when its dark signal changed significantly. In-between these instants, the two components were assumed to be constant, and a robust linear regression, with respect to integration time, provides first estimates and a quality coefficient. The latter serves to assign definitive estimates for this pixel and that period. Results. Our model is part of the SODISM Level 1 data production scheme. To confirm its reliability, we verified on dark frames that it leaves a negligible residual bias (5 e -) and generates a small rms error (25 e- rms). We also examined the distribution of the image zone dark current. The cool pixel level is found to be 4.0 e- pxl-1 s-1, in agreement with the predicted value. The emergence rate of hot pixels was investigated as well. It yields 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, that is, 4.2% of the image zone area per year. Conclusions. A new method for dark signal correction of a frame-transfer CCD operating near -10 °C is described and applied. It allows making recommendations about the implementation and scientific usage of such CCDs. Moreover, aspects of the method (adaptation of the unbalanced Haar technique, dedicated robust linear regression) have a generic interest. © ESO, 2013. [less ▲]

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See detailBAGIDIS: Statistically investigating curves with sharp local patterns using a new functional measure of dissimilarity
Timmermans, Catherine ULiege; von Sachs, Rainer

E-print/Working paper (2013)

A functional wavelet-based semi-distance is defined for comparing curves with misaligned sharp local patterns. It is data-driven and highly adaptive to the curves. A main originality is that each curve is ... [more ▼]

A functional wavelet-based semi-distance is defined for comparing curves with misaligned sharp local patterns. It is data-driven and highly adaptive to the curves. A main originality is that each curve is expanded in its own wavelet basis, which hierarchically encodes the patterns of the curve. The key to success is that variations of the patterns along the abscissa and ordinate axes are taken into account in a unified framework. Associated statistical tools are proposed for detecting and localizing differences between groups of curves. This methodology is applied to 1H-NMR spectrometric curves and solar irradiance time series. [less ▲]

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See detailAdvantages of the Bagidis methodology for metabonomics analyses: application to a spectroscopic study of Age-related Macular Degeneration
Timmermans, Catherine ULiege; De Tullio, Pascal ULiege; Lambert, Vincent et al

in Proceedings of the 12th European Symposium on Statistical Methods for the Food Industry (2012, February 29)

The Bagidis methodology proposes a distance measure between spectra, that takes into account, in a unified framework, both horizontal shifts and amplitudes variations that might affect spectral peaks. The ... [more ▼]

The Bagidis methodology proposes a distance measure between spectra, that takes into account, in a unified framework, both horizontal shifts and amplitudes variations that might affect spectral peaks. The method relies on the expansion of the spectra in unbalanced Haar wavelet bases. Its opportunity for investigating 1HNMR spectra in metabonomics is illustrated here in the framework of a study of an eye disease: age-related macular degeneration. Visual analysis, disease detection model and search for biomarkers are proposed here and compared with known methods. [less ▲]

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See detailShah: Shape-Adaptive Haar Wavelet Transform For Images With Application To Classification
Timmermans, Catherine ULiege; Fryzlewicz, Piotr

E-print/Working paper (2012)

We propose the SHAH (SHape-Adaptive Haar) transform for images, which results in an orthonormal, adaptive decomposition of the image into Haar-like components, arranged hierarchically according to ... [more ▼]

We propose the SHAH (SHape-Adaptive Haar) transform for images, which results in an orthonormal, adaptive decomposition of the image into Haar-like components, arranged hierarchically according to decreasing importance, whose shapes reflect the features present in the image. The decomposition is as sparse as it can be for piecewise-constant images. It is performed via an iterative bottom-up algorithm with quadratic computational complexity; however, nearly-linear variants also exist. SHAH is rapidly invertible. We use SHAH to define the BAGIDIS semi-distance between images. It compares both the amplitudes and the locations of the SHAH components of the images and is flexible enough to account for feature misalignment. Performance of the SHAH+BAGIDIS methodology is illustrated in regression, classification and clustering problems and shown to be very encouraging. A clear asset of the methodology is its very general scope: it can be used with any images or more generally with data that can be described as graphs or networks. [less ▲]

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See detailUsing Bagidis in nonparametric functional data analysis: Predicting from curves with sharp local features
Timmermans, Catherine ULiege; Delsol, Laurent; von Sachs, Rainer

in Journal of Multivariate Analysis (2012), 115

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 ... [more ▼]

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. [less ▲]

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See detailInvestigating functional data with sharp local features with applications to spectroscopy
Timmermans, Catherine ULiege

Doctoral thesis (2012)

This thesis aims to tackle some common but challenging issues in 1H NMR spectroscopic studies: investigating differences between some groups of spectra, determining a statistical model for the prediction ... [more ▼]

This thesis aims to tackle some common but challenging issues in 1H NMR spectroscopic studies: investigating differences between some groups of spectra, determining a statistical model for the prediction of a measured quantity or a group membership associated to a spectrum, and identifying the zones of the spectra that carry significant information for the discrimination. Statistically, this requires the study of curves with sharp local features, those features being peaks associated to given resonance frequencies in the spectra, and of which the intensity reflects the concentration of given chemical compounds. A challenge in this problem is to define an efficient measure of the dissimilarity between the spectra. Indeed, a given peak in a dataset of spectra might be affected by vertical amplifications, horizontal shifts or both simultaneously, the source of those variations possibly being a significant chemical difference or resulting from noise. However, commonly used dissimilarity measures do not return coherent results as soon as there is a horizontal component of variation from one spectrum to another. This thesis proposes therefore a new dissimilarity measure which has the ability to capture both horizontal and vertical variations of the peaks in datasets of spectra, in a unified framework. This dissimilarity measure has been called BAGIDIS for Bases GIving DIStances. BAGIDIS provides for a new methodology in the context of nonparametric functional statistics. The method has a sound theoretical background as it fully takes into account three cutting-edge statistical concepts: the nature of functional data, the nonparametric functional regression technique and unbalanced Haar wavelets. Moreover, it is not restricted to the analysis of spectra, but enlarges to any functional dataset with sharp local features that might possibly be misaligned. An extension exists for images. [less ▲]

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See detailBases Giving Distances. A New Semimetric and its Use for Nonparemetric Functional Data Analysis
Timmermans, Catherine ULiege; Delsol, Laurent; von Sachs, Rainer

in Ferraty, Frédéric (Ed.) Recent Advances in Functional Data Analysis and Related Topics (2011)

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 ... [more ▼]

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. [less ▲]

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See detailBAGIDIS, a new method for statistical analysis of differences between curves with sharp discontinuities
Timmermans, Catherine ULiege; von Sachs, Rainer

E-print/Working paper (2010)

In this paper, we introduce a functional wavelet based semi-distance for comparing curves with sharp patterns that might not be well aligned from one curve to another. This semi-distance is data-driven ... [more ▼]

In this paper, we introduce a functional wavelet based semi-distance for comparing curves with sharp patterns that might not be well aligned from one curve to another. 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, which proofs highly useful when used together with clustering algorithms or visualization method. We also develop statistical tools for detecting and localizing differences between groups of curves using this semi-distance. Finally, we apply this methodology to H-NMR spectrometric curves and solar irradiance time series. [less ▲]

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See detailComparaison et classifications de séries temporelles via leur développement en ondelettes de Haar asymétriques
Timmermans, Catherine ULiege; Delouille, V.; von Sachs, Rainer

in Actes des XVIe rencontres de la Société Francophone de Classification (2009)

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