44 The Dantzig Selector for Censored Linear Regression Models: Identifying Predictive Genes for Myeloma Disease Progression Yi Li Dana-Farber Cancer Institute, Harvard School of Public Health Friday January 8, 2010 15h00 ? V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

The Dantzig variable selector has recently emerged as a powerful tool for fitting regularized regression models. A key advantage is that it does not pertain to a particular likelihood or objective function, as opposed to the existing penalized likelihood methods, and hence has the potential for wide applications. To our knowledge, almost all the Dantzig selector work has been performed with fully observed response variables. This talk introduces a new class of adaptive Dantzig variable selectors for linear regression models when the response variable is subject to right censoring. This is motivated by a clinical study of detecting predictive genes for myeloma patients' event-free survival, which is subject to right censoring. We establish the theoretical properties of our procedures, including consistency in model selection (i.e. the right subset model will be identified with a probability tending to 1) and the oracle property of the estimation (i.e. the asymptotic distribution of the estimates is the same as that when the true subset model is known a priori). The practical utility of the proposed adaptive Dantzig selectors is verified via extensive simulations. We apply the new method to the aforementioned myeloma clinical trial and identify important predictive genes for patients' event free survival. 43 Testing for association between a genetic marker and disease status using family data Gudrun Jonasdottir Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm Friday December 8, 2006 14h30 15h30 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Diseases with a genetic component tend to "cluster"dels in genetic association studies and use of offsets in FBAT and the proposed score test. 42 Sensitivity Analysis after Multiple Imputation under Missing At Random -A Weighting Approach James Carpenter The London School of Hygiene and Tropical Medicine Thursday May 4, 2006 16h30 17h30 V3

Krijgslaan 281, Building S9, 9000 Gent, Belgium

ation under MAR. This provides ball-park estimates of the results of full NMAR modelling, indicating the extent to which it is necessary and providing a check on its results. We illustrate our approach with a small simulation study, and the analysis of data from a large multi-centre clinical trial. 41 Developing an E-course for future students of the Master in Statistical Data Analysis Fanghong Zhang Ghent University Wednesday November 9, 2005 13h30 14h30 A3

Krijgslaan 281, Building S9, 9000 Gent, Belgium

abstract09nov05.pdf 40 Spatial Clustering Detection for Censored Outcomes: A Cumulative Martingale Residual Approach Yi Li Harvard University and Dana-Farber Cancer Insitute, U.S.A. Monday July 4, 2005 14h00 15h00 V1

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Numerous methods that have been proposed to test for spatial clustering, particularly for binary or continuous outcomes. However none has been proposed for outcomes which are subject to censoring. This project provides an extension of the spatial scan statistic (Kulldorff, 1997) for data with failure time outcomes using the log rank test statistic. It further proposes an extension of the cumulative geographic residual method that utilizes the model diagnostic techniques for censored outcomes. Application of these methods will be illustrated by the Home Allergens and Asthma prospective cohort study analyzed the relationship of environmental exposures with asthma, allergic rhinitis/hayfever, and eczema. 39 A robust fit for Generalized Additive Models Matias Salibian-Barrera University of British Columbia Monday May 23, 2005 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Generalized Additive Models (GAM) (Hastie and Tibshirani, 1986, 1990) are a powerful exploratory tool that is widely used in practice. Unfortunately, popular fitting algorithms for these models (e.g. the General Local Scoring Algorithm (GLSA), Hastie and Tibshirani, 1990) can be highly sensitive to a small proportion of observations that depart from the model.
In this talk I will describe a new robust fit for GAM models. The building blocks of this proposal are robust estimates for Quasi-Likelihood (QL) models (Cantoni and Ronchetti, 2001b; see also Stefanski, Carroll and Ruppert, 1986; and K\"unsch, Stefanski and Carroll, 1989) and the GLSA algorithm (Hastie and. Tibshirani, 1986, 1990). Specifically, we adapt the GLSA algorithm using robust estimating equations to determine appropriate weights that transform the robust QL score equations into re-weighted least squares equations. We then iteratively fit weighted additive models, in the same spirit as GLSA. Bandwidth selection can be done automatically using a robust cross-validation criteria (Ronchetti and Staudte, 1994; Cantoni and Ronchetti, 2001).
This method will be illustrated on real and synthetic data. Simulation results suggest that the fit obtained with this algorithm is able to resist the effect of outliers in a number of different situations and that it also performs well when there are no atypical observations. 38 Graphical Diagnostics for Lack-of-Fit in Regression Models Ellen Deschepper Ghent University Friday 4 March 2005 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

abstract04mar05.pdf 37 Comparison of two structural modeling approaches to estimate the effect of Hormone Therapy Krista Fischer Tartu University, Estonia Friday 11 February 2005 14h00 15h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Not available 36 A Bayesian approach to jointly estimate center and treatment by center heterogeneity in a proportional hazards model Catherine Legrand European Organization for Research and Treatment of Cancer Friday 11 February 2005 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

abstract11feb05.pdf 35 Probability Models for Nonnegative Random Variables, with Application in Survival Analysis and Reliability Ingram Olkin Stanford University Monday 31 January 2005 17h00 18h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Nonnegative random variables arise in a wide variety of applications; in particular in reliability and survival analysis. Whereas for random variables on the whole line the normal distribution plays a distinctive role, for nonnegative random variables there is no distribution as pervasive as the normal distribution with it s foundation in the central limit theorem.

We consider three classes of distributions: nonparametric, semiparametric and parametric families. Examples from each class are logconcave density families, increasing hazard rate families, and the Weibull distribution, respectively. We provide a survey of these three classes of families with an emphasis on the behavior of their hazard rates and on stochastic orderings.

Joint work with Albert Marshall.

34 Analysis of microarray data in a dose-response setting Ziv Shkedy Limburgs Universitair Centrum Thursday 27 January 2005 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

DNA microarrays have been recently used for the purpose of monitoring expression levels of thousands of genes simultaneously, and identifying those genes that are differentially expressed. As a result type I error (the probability for false identification) increase sharply when the number of tested genes gets large. In this talk we focus on a dose-response setting in which cDNA microarrays are available for four dose levels (3 microarrays at each dose level). A gene is differentially expressed if there is a trend (with respect to dose) of the gene intensity. We discuss several approaches to test the null hypothesis of no dose effect versus an order alternative. Resampling based False Discovery Rate (FDR) and resampling Family-Wise Error Rate (FWER) are used for controlling type I error. 33 Graphical Diagnostics for Lack-of-Fit in Regression Models Ellen Deschepper Ghent University Friday 17 December 2004 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

abstract17dec04.pdf 32 Applying Methods from Machine Learning to Insurance Problems Andreas Christmann University of Dortmund Monday 22 November 2004 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

abstract22nov04.pdf 31 Analysis of Developmental Toxicity Data Christel Faes Limburgs Universitair Centrum Friday 12 November 2005 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

abstract12nov04.pdf 30 Fast bootstrap methods for robust estimators Gert Willems Department of Mathematics and Computer Science, UA Friday 22 October 2004 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

There are several situations in regression analysis, or multivariate location/scatter problems, where it is desirable to use robust estimators instead of the classical estimators. The idea of robust estimators is that they are able to resist contamination (e.g. in the form of so-called outliers) present in the dataset. In addition they should preferably be as efficient as possible in case of "clean" data.
Since over 30 years now, a vast literature has come into being on such estimators, but the inference part is often being neglected. That is, often it is not clear how to obtain a reliable estimate for the variability of the estimator, or how to obtain accurate confidence limits for parameters that are being estimated. Mostly, asymptotic variance results are used for this purpose. However, such asymptotic estimates may be inaccurate for small sample sizes and often are clearly inappropriate in situations where robust estimators are recommended.
Resampling methods such as Efron's bootstrap constitute an alternative to the asymptotic approach. Some drawbacks arise though when applying the classical bootstrap to robust estimators. The most serious of these is the computational cost of the bootstrap procedure. Indeed, computing robust estimators often takes time-consuming algorithms and the classical bootstrap method requires the estimator to be recalculated many times. A second aspect of concern is that bootstrap inference can easily be adversely affected by contamination, even if the estimator itself was able to resist all outliers.
Several general approaches for handling the robustness problem are available. A general procedure to speed up the bootstrap method however is not. In this talk, some fast and robust bootstrap methods will be presented. In particular, methods for the popular Least Trimmed Squares and Minimum Covariance Determinant estimators will be considered, as well as for S-estimators and MM-estimators. 29 A history of smooth tests of goodness of fit J.C.W. Rayner School of Mathematics and Applied Statistics
University of Wollongong, Australia Friday 17 September 2004 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Goodness of fit testing is briefly reviewed, starting from the landmark paper of Pearson (1900). The smooth tests were introduced by Neyman (1937), but Pearson's so called X2 test is a smooth test. Emphasis is given to the recent work of Rayner and Best, and now Thas, which focuses on interpretability and more complex inference. 28 Robust Variable Selection Ruben Zamar University of British Columbia Friday 14 May 2004 13h00 14h00 A3

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Robust model selection has not received much attention in the robustness literature. A few papers that address this issue include (Ronchetti 1984) and (Ronchetti and Staudte 1994), where the authors robustify the normal-theory selection criteria AIC and Cp, respectively. Morgenthaler et al. (2003) propose a selection technique to identify the correct model structure as well as unusual observations. Ronchetti et al. (1997) propose robust model selection by cross-validation.

One major drawback of robust model selection tools is that they are in general computationally intensive and time consuming, as they require the fitting of all submodels. One exception is the model selection based on the Wald test (Sommer and Huggins, 1996) which requires the computation of estimates only from the full model. However, fitting the `full' model may not be reasonable or computationally feasible.

In this study, we focus our attention on the robustification of Stepwise Regression. This will provide us with a robust ordering of the covariates so that we can choose a number of predictors from the top of the list. Efron et al. (2003) propose Least Angle Regression (LARS), a promising normal-theory algorithm that has clear advantages over the Forward Selection and the Forward Stagewise procedures. We illustrate the sensitivity of LARS to outliers and present two different approaches to its robustification. The robust LARS is computationally suitable because we can avoid the fitting of all the submodels and the full model.

References
Efron, B.E., Hastie, T., Johnstone, I. and Tibshirani, R. (2004). Least Angle regression. Annals of Statistics, to appear.

Morgenthaler, S., Welsch, R.E. and Zenide, A. (2003) Algorithms for robust model selection in linear regression. ICORS 2003 proceedings.

Ronchetti, E. (1985). Robust model selection in regression. Statistics and Probability Letters, 3, 21-23.

Ronchetti, E. and Staudte, R.G. (1994). A robust version of Mallow's Cp. Journal of the American Statistical Association, 89, 550-559.

Ronchetti, E., Field, C. and Blanchard, W. (1997) Robust linear model selection by cross-validation. Journal of the American Statistical Association, 92, 1017-1023.

Sommer, S. and Huggins, R.M. (1996). Variable selection using the Wald Test and Robust $C_{p}$. J.R. Statist. Soc., B, 45, 15-29.

27 Screening for Potentially Informative Dropout in Longitudinal Studies with Binary Outcome Tom Loeys Merck Sharp & Dohme, Brussels, Belgium Friday 7 May 2004 14h00 15h00 A3

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Longitudinal studies are often faced with drop-out. The natural question arising is then whether the time to drop-out is associated with the longitudinal trajectory. Following Henderson et al. (Biostatistics, 2000), we address that question through joint modeling approaches for the time to drop-out and the longitudinal process. The proposed methodology is applied to a clinical trial in the acute treatment of migraine. Especially we present a sensitivity analysis exploring the impact on the inference from the longitudinal data model when the time to drop-out from the study is related to the patient's headache relief trajectory through unobserved covariates. 26 Missing Data Methods for Structural Equation Models Peter M. Bentler Departments of Psychology & Statistics
University of California, Los Angeles Wednesday 14 April 2004 16h00 17h00 Auditorium A

H. Dunantlaan 1, 9000 Gent, Belgium

Traditional, newer and newest methods for the analysis of structural equation models based on incomplete data are discussed. Among the traditional methods are listwise deletion, pairwise present analyses, mean imputation, regression imputation, hot deck imputation. Among newer methods are the expectation-maximization (EM) algorithm and case-wise or direct maximum likelihood. Among the newest are statistically justified pairwise present methods, pseudo or robust maximum likelihood, and tests of homogeneity of means and covariances. Simulation studies of the performance of the newer and newest methods are discussed. Interpreting the role and limitations of these methods involves concepts of MCAR (missing completely at random) and MAR (missing at random). Implementation via EQS 6 is mentioned. 25 The valuation of Asian options for market models of exponential Levy type Hansjörg Albrecher Department of Mathematics B, Graz University of Technology, Austria Friday 2 April 2004 13h30 14h30 A3

Krijgslaan 281, Building S9, 9000 Gent, Belgium

We consider asset price processes of exponential Levy type and derive various approximations and bounds for the Esscher and the mean-correcting price of European arithmetic and geometric average options. Furthermore, a static super-hedging strategy based on comonotonicity is developed. Numerical illustrations of the accuracy of these bounds and approximations are given for normal inverse Gaussian and variance-gamma distributed log returns, respectively. Finally, we compare the option prices in these models with the corresponding Black-Scholes prices. 24 Beyond ignorance: evaluating the plausibility of possible parameter estimates and inferences when data are missing James Carpenter London School of Hygiene and Tropical Medicine Friday 6 February 2003 13h00 14h00 A3

Krijgslaan 281, Building S9, 9000 Gent, Belgium

When some of the intended responses in a study are unobserved, an extra element of uncertainty is introduced into the analysis in addition to the familiar sampling imprecision.

In order to obtain parameter estimates, and make inferences, assumptions have to be made. It is thus important to examine the sensitivity of inference to these assumptions. If missing responses are categorical, a natural approach is to replace the usual point estimate with the range of estimates corresponding to all possible completions of the data. This leads naturally to optimistic/pessimistic bounds for a parameter, known as an interval of ignorance.
However, often the distribution of parameter estimates within this interval is far from uniform. We therefore develop and describe two approaches applicable to discrete data modelled using generalised linear models.
The first we term the Estimates Above a Threshold (EAT) algorithm. This permits calculation of the proportion of parameter estimates which lie above a threshold under the analyst's choice of probability distribution for the missing data.
The second approach enables the calculation of the expected p-value, again under the analyst's choice of probability distribution for the missing data.

We illustrate our ideas with data from a randomised controlled trial (RCT) of different doses of a pain killer following molar extraction and a recent RCT of interventions to improve the quality of peer review.

This is joint work with Claudio Verzilli, Imperial College London.

23 Developments in Longitudinal Studies Geert Verbeke Biostatistical Centre, K.U.Leuven, Belgium Friday 5 December 2003 13h00 14h00 A2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Nowadays, linear mixed models (Verbeke and Molenberghs, 2000) are probably the most frequently used models for the analysis of continuous longitudinal data or repeated measurements. This class of models has deserved a lot of attention in the statistical literature, and is available in a wide variety of commercially available software packages (e.g., SAS, SpluS, ...).

Still, many of the statistical properties such as interpretation of the parameters, and sensitivity with respect to model misspecifications have deserved relatively little attention. In this presentation, some of these aspects will be looked at in more detail. It will be shown that, depending on the inferences of interest, inferences may or may not be highly affected
by wrong distributional assumptions or by omission of important covariates. In cases of sensitivity, we will show how more robust inferences can be obtained from extended versions of the classical model.

Finally, some aspects of model formulation and parameter interpretation will be discussed. Linear mixed models can be interpreted hierarchically or marginally, and this has important consequences with respect to estimation and inference. Here, the likelihood ratio test and the score test will be discussed and compared in the context of linear mixed models.

All results will be extensively illustrated using real data.

Reference:
Verbeke, G. and Molenberghs, G. (2000). Linear mixed models for longitudinal data. Springer Series in Statistics, Springer-Verlag, New-York.

22 Methodology for genetic analyses of twins and families Sylvie Goetgeluk Ghent University
Department of Applied Mathematics and Computer Science Friday 31 October 2003 13h00 14h00 A2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Not available - Reading Club 21 Functionals of clusters of extremes Johan Segers Tilburg University
Department of Econometrics and OR Friday 24 October 2003 13h00 14h00 A2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

For arbitrary stationary sequences of random variables satisfying a mild mixing condition, distributional approximations are established for functionals of clusters of exceedances over a high threshold. The approximations are in terms of the distribution of the process conditionally on the event that the first variable exceeds the threshold. This conditional distribution is shown to converge to a non-trivial limit if the finite-dimensional distributions of the process are in the domain of attraction of a multivariate extreme-value distribution. In this case, therefore, limit distributions are obtained for functionals of clusters of extremes, thereby generalizing results for higher-order stationary Markov chains by S. Yun (2000), J. Appl. Probab. 37, 29--44. 20 The Shared Frailty Model Paul Janssen Center for Statistics
Limburgs Universitair Centrum Friday 12 September 2003 11h00 12h00 V3

Krijgslaan 281, Building S9, 9000 Gent, Belgium

We first show the usefulness of frailty models to describe multivariate survival data and we describe the importance of the heterogeneity parameter (the parameter of the frailty density).

In a second part we review some aspects of statistical inference for frailty models.

Finally we discuss the asymptotic behaviour of the likelihood ratio test for heterogeneity in shared frailty models.

19 Semiparametric regression for repeated outcomes with nonignorable intermittent nonresponse S. Vansteelandt, A. Rotnitzky and J. Robins Ghent University, Gent, Belgium and Harvard School of Public Health, Boston, USA Wednesday 18 June 2003 16h00 17h00 A3

Krijgslaan 281, Building S9, 9000 Gent, Belgium

We examine a new class of models for making inference about the mean of a vector of correlated outcomes when the outcome vector is incompletely observed in some study units and missingness is non-monotone.
Each model in our class is indexed by a set of unidentified selection bias functions which quantify the residual association of the outcome at each occasion t and the probability that this outcome is missing at the t-th occasion after adjusting for variables observed prior to time t and the past non-response pattern. In particular, selection bias functions equal to zero encode the investigators a priori belief that non-response of the next outcome can be entirely explained by the observed past. We call this assumption sequential explainability.
Because each model in our class is non-parametric, it fits the data perfectly well. As such, our models are ideal for conducting sensitivity analyses aimed at evaluating the impact that different degrees of departure from sequential explainability have on inference about the marginal means of interest. Sensitivity analysis is conducted by examining how inference about the marginal means changes as one varies the selection bias functions regarded as known under each model.
We then extend our proposed class of models to incorporate: 1) data configurations which include baseline covariates and, 2) a parametric model for the conditional mean of the vector of correlated outcomes given the baseline covariates. We describe a class of estimators for the parameter indexing the conditional mean model, which up to asymptotic equivalence, comprise all consistent and asymptotically normal estimators of this parameter under the postulated model for non-response in the class. Finally, we describe a nearly efficient estimator of this parameter. 18 Multiscale triangulations and second generation wavelets in nonlinear smoothing of scattered data Maarten Jansen Technische Universiteit Eindhoven
Department of Mathematics and Computer Science Friday 13 June 2003 13h00 14h00 A3

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Wavelet techniques have gained a considerable popularity in non-parametric data smoothing. The very construction of classical wavelets makes them intrinsically unsuited for non-equispaced data. Some efforts have been made to deal with this problem in 1-d (one dimension). First, this talk presents the application of the so called lifting scheme (by Sweldens), which allows for a natural extension of wavelet theory towards irregularly sampled data, both in 1-d and 2-d. Secondly, for the 2-d case, we present a multiscale (Delaunay) tesselation of the data, and a corresponding wavelet transform, based on Sibson interpolation. A third element of our approach is the actual estimation on wavelet coefficients. We discuss simple thresholding as well as a Bayesian procedure, based on the Johnstone-Silverman model. Finally, we discuss some issues of numerical condition related to this wavelet transform. 17 Mapping Soil Texture at a Regional Scale using Pedometrical Techniques Marc Van Meirvennne Department Soil Management and Soil Care
Faculty of Agricultural and Applied Biological Sciences, Ghent University Friday 06 June 2003 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Pedometrical techniques are numerical methods used to describe and analyze soil properties in a quantitative way. Frequently these are related to spatial inventory, space-time modeling and statistical survey. We used such techniques to map soil texture at a regional scale.

The study area covered about 3000 km2 and is located in Belgium. It was selected because it contains a large range of soil types with different geological histories. In total 4887 topsoil samples were analyzed for soil texture and a 1/100.000 choropleth soil texture map (based on the Belgian soil texture classification) was also available. However, an update of this map was required, as well as a reclassification according to the internationally accepted USDA texture triangle. Moreover, a quantitative map of the three major soil textural classes (clay, silt and sand) was needed as input for GIS linked models.

To map the three textural fractions quantitatively we used compositional kriging, which is a version of ordinary kriging to which some conditions were added. One of these conditions is that the sum of the three fractions must equal 100, which is not ensured when each fraction is interpolated independently. The data set was stratified according to the delineations of the choropleth soil map. These delineations represent either crisp physical boundaries or transition zones. Therefore, different stratification and interpolation strategies were followed according to the nature of the map boundaries. The resulting maps were classified according the both the Belgian and the USDA textural triangles allowing for the first time a comparison between both classification systems.

Finally a sensitivity analysis was conducted to explore the uncertainty related tot the textural classification. Therefore a Monte Carlo analysis was used based on the kriging variance of the predictions of each textural fraction. This information can be used in a GIS whenever the mapping quality of the classified maps is required.

16 Model averaging, post-model selection inference and the focussed information criterion Gerda Claeskens Universitair Centrum Limburg Tuesday 27 May 2003 15h30 16h30 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

The traditional use of model selection methods in practice is to proceed as if the final selected model had been chosen a priori, without acknowledging the additional uncertainty introduced by model selection. This often means underreporting of variability and too optimistic confidence intervals, for example. In addition to quantifying the implied cost of model selection involved in AIC and similar procedures, I give results for estimators that smooth across many models. This amounts to a frequentist parallel to the Bayesian model averaging methods.
In a second part of the talk I take the view that the model selector should focus on the parameter singled out for interest; in particular, a model which gives good precision for one estimand may be worse when used for inference for another estimand. This yields a focussed information criterion, the FIC.
This is joint work with Nils Lid Hjort. 15 Inference on Survival Data with Covariate Measurement Error-An Imputation-based Approach Yi Li Harvard School of Public Health Tuesday 20 May 2003 16h00 17h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

We propose a new method for fitting proportional hazards models with error-prone covariates. Regression coefficients are estimated by solving an estimating equation that is the average of the partial likelihood scores based on imputed true covariates. For the purpose of imputation, a local spline model is assumed on the baseline hazard.
We discuss consistency and asymptotic normality of the resulting estimators, and propose a stochastic approximation scheme to obtain the estimates. The algorithm is easy to implement, and reduces to the ordinary Cox partial likelihood approach when the measurement error has a degenerate distribution. Simulations indicate high efficiency and robustness. We consider the special case where error-prone replicates are available on the unobserved true covariates. As expected, increasing the number of replicates for the unobserved covariates increases efficiency and reduces bias.

We illustrate the practical utility of the proposed method with an Eastern Cooperative Oncology Group clinical trial where a genetic marker, c-myc expression level, is subject to measurement error.

14 Reporting and Statistics using SAS Enterprise Guide Saar De Zutter Department of Applied Mathematics and Computer Science
Ghent University Friday 9 May 2003 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Not available - READING CLUB 13 The implementation of cancer registration in Belgium:
a never ending story ? Joost Weyler Department Epidemiologie
Faculteit Geneeskunde, Antwerpen University Friday 25 April 2003 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Belgium was one of the first countries in Europe to start with cancer registration on a national basis. However, there are important indications of poor quality of the registered data. Based on the experience in the Netherlands different provincial cancer institutes emerged. One of the aims of these institutes was to set up regional cancer registries. To date two cancer registries have emerged from these initiatives. One, the Limburg Cancer registry (LIKAR) collects data from all pathologists in the province of Limburg. The other one, the Antwerp Cancer Registry (AKR), is based on active registration by data nurses. Despite the high quality of the registered data, the future for these registries is uncertain. 12 The analysis of QoL data Kristel Van Steen Biostatistics, Center for Statistics
Limburgs Universitair Centrum Friday 11 April 2003 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

When evaluating the efficacy of medical treatment on cancer, prolongation of life expectancy and tumor shrinkage have traditionally been taken as outcome measures. Despite the substantial side effects and functional impairment often associated with cancer treatment, it is only recently that attention has been given to the assessment of quality of life (QoL). This increasing interest in QoL has important implications for clinical trials, as careful planning is required at all stages of a study from protocol design through reporting of results.

Following a classical protocol layout, we will discuss protocol contents relevant to QoL assessment. These topics include (1) the choice of QoL scale scoring system, (2) timing of assessments, (3) the mode of data collection, and (4) statistical considerations with an emphasis on data analysis methods.

Several different scales are common in QoL research, as there are Likert scales, Visual Analogue Scales (VAS), adjectival scales. In this talk special attention is given to the EORTC QLQ-C30 questionnaire with multi-item scales and single item measures.
Obviously, analyzing quality of life data from these questionnaires may be complicated for several reasons. Quality of life data not only involves repeated measures, but it is also usually collected on ordered categorical responses. In addition, it is evident that not all patients provide the same number of assessments, due to attrition caused by death or other medical reasons. Some patients may fail to answer only a few questions or items on the questionnaire.

mptions will drive the analysist in the choice of an appropriate model and estimation technique.

ng the direction or magnitude of effects of the predictor variable on the response variable, even with a correct model specification. It is self-evident that these issues need to be addressed before drawing conclusions from a prognostic factor analysis. Model instability in prognostic factor analyses will be illustrated via bootstrap procedures and model-averaging methodology on data generated from EORTC phase III clinical trials.

11 The S-Language Saar De Zutter Department of Applied Mathematics and Computer Science
Ghent University Friday 14 March 2003 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Not available - READING CLUB 10 Score tests for detecting linkage to quantitative traits Hein Putter Leids University Medical Centre Friday 28 February 2003 16h00 17h00 A0

Krijgslaan 281, Building S9, 9000 Gent, Belgium

This talk is concerned with statistical methods to localize genes responsible for quantitative traits, i.e. for "diseases" that can be measured on a quantitative scale, such as high blood pressure, cholesterol. The location of such a gene is called a quantitative trait locus (QTL). The first step in the search for QTLs is a "linkage analysis"omponents likelihood ratio test is also asymptotically equivalent to this optimal Haseman-Elston test. This fact gives a theoretical explanation of the empirical observation from simulation studies reporting similar power of the variance components likelihood ratio test and the optimal Haseman-Elston method. If time permits, I will discuss extensions to support the simultaneous analysis of more than two loci and multivariate phenotypes. 9 Modelling family data: from segregation to linkage analysis Jeanine Houwing-Duistermaat University Rotterdam Friday 24 January 2003 13h00 14h00 A2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Not available 8 Introduction to Robust Statistics Stefan Van Aelst Ghent University Friday 17 January 2003 13h00 14h00 A2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Not available - READING CLUB 7 The challenge of patient choice and non-adherence to treatment in RCTS of counselling and psychotherapy Graham Dunn University of Manchester Friday 10 January 2003 13h00 14h00 A2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Patient preferences, beliefs and motivation influence compliance to the psychological interventions in RCTs in psychiatry, and compliance in its turn is an important predictor of loss to follow-up. It has also been suggested that preferences themselves may also modify the effects of the treatment received (Brewin & Bradley, BMJ 299, 313-5, 1989). Using a counterfactual causal model, I will illustrate the estimation the Complier-Average Causal Effect (CACE) in a mult-centre psychotherapy trial for depression (ODIN: Dowrick et al., BMJ 321, 1450-4, 2000) which was subject to both lack of compliance and loss to follow-up. I will then ask how such methods might help us to valuate the potential of partially-randomized 'patient preference' designs (Brewin & Bradley, 1989) and to compare them with other, ossibly more promising, alternatives. 6 Structural accelerated failure time models and recurrent events An Vandebosch Ghent University Friday 13 December 2002 13h00 14h00 A2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Not available 5 Het schatten van parameters in rentevoetmodellen Ella Roelant Ghent University Friday 6 December 2002 13h00 14h00 A2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Not available - PRESENTATION IN DUTCH 4 Testability of the Coarsening At Random (CAR) assumption Eric Cator Delft University of Technology Monday 18 November 2002 16h00 17h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Not available 3 Causal graphs in epidemiology Stijn Vansteelandt Ghent University Wednesday 6 November 2002 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Not available - READING CLUB 2 A frailty model for HIV infection in mobile and non-mobile cohorts from a rural district of South Africa Khangelani Zuma University of Waikato, New Zealand Friday 20 September 2002 14h00 15h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

Not available 1 Improving response prediction in direct marketing by optimizing for specific mailing depths Van den Poel, A. Prinzie & P. Van Kenhove Department of Marketing
Ghent University, Belgium Friday 20 September 2002 13h00 14h00 V2

Krijgslaan 281, Building S9, 9000 Gent, Belgium

sion by iteratively changing the true values of the dependent variable during the maximum-likelihood estimation procedure. Those customers who rank lower than the cutoff in terms of predicted purchase probability, imposed by the mailing-depth restriction, will not contribute to the total likelihood. We illustrate our procedure on a real-life direct-marketing dataset comparing traditional response models to our innovative approach optimising for a specific mailing depth. The results show that for mailing depths up to 48% our method achieves significant and substantial profit increases.