References of "Molenberghs, G"
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See detailMulticollinearity
Van Steen, Kristel ULg; Molenberghs, G.

in Chow (Ed.) Encyclopedia of Biopharmaceutical Statistics (2010)

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See detailAnalysis of incomplete data
Molenberghs, G.; Beunkens, C.; Thijs, H. et al

in SAS System for Clinical Trials II (2007)

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See detailApproaches to handling incomplete data in family-based association testing
Van Steen, Kristel ULg; Laird, N. M.; Markel, P. et al

in Annals of Human Genetics (2007), 71(Pt 2), 141-51

The high throughput of data arising from the complete sequence of the human genome has left statistical geneticists with a rich and extensive information source. The wide availability of software and the ... [more ▼]

The high throughput of data arising from the complete sequence of the human genome has left statistical geneticists with a rich and extensive information source. The wide availability of software and the increase in computing power has improved the possibilities to access and process such data. One problem is incompleteness of the data: unobserved or partially observed data points due to technical reasons or reasons associated with the patient's status or erroneous measurements of phenotype or genotype, to name a few. When not properly accounted for, these sources of incompleteness may seriously jeopardize the credibility of results from analyses. In this paper we provide some perspectives on the occurrence and analysis of different forms of incomplete data in family-based genetic association testing. [less ▲]

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See detailAnalysis of incomplete data
Molenberghs, G.; Beunkens, C.; Jansen, I. et al

in 'SAS System for Clinical Trials II (2005)

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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 detailIntroducing the multivariate dale model in population-based genetic association studies
Van Steen, Kristel ULg; Tahri, N.; Molenberghs, G.

in Biometrical Journal = Biometrische Zeitschrift (2004), 46(2), 187-202

Until recently, the most common parametric approaches to study the combined effects of several genetic polymorphisms located within a gene or in a small genomic region are, at the genotype level, logistic ... [more ▼]

Until recently, the most common parametric approaches to study the combined effects of several genetic polymorphisms located within a gene or in a small genomic region are, at the genotype level, logistic regressions and at the haplotype level, haplotype analyses. An alternative modeling approach, based on the case/control principle, is to regard exposures (e.g., genetic data such as derived from Single Nucleotide Polymorphisms - SNPs) as random and disease status as fixed and to use a marginal multivariate model that accounts for inter-relationships between exposures. One such model is the multivariate Dale model. This model is based on multiple logistic regressions. That is why the model, applied in a case/control setting, leads to straightforward interpretations that are similar to those drawn in a classical logistic modeling framework. [less ▲]

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See detailMulticollinearity
Van Steen, Kristel ULg; Molenberghs, G.

in Chow, Shein-Chung (Ed.) Encyclopedia of Biopharmaceutical Statistics (2004)

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See detailMultivariate and multidimensional analysis
Van Steen, Kristel ULg; Molenberghs, G.

in Wilson (Ed.) Biometrics (2003)

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See detailThe multivariate Dale model and genetic associations
Van Steen, Kristel ULg; Molenberghs, G.; Tahri, N.

in American Journal of Human Genetics (2002), 71

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See detailMulticollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection
Van Steen, Kristel ULg; Curran, D.; Kramer, J. et al

in Statistics in Medicine (2002), 21(24), 3865-3884

Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to ... [more ▼]

Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component. suggesting that it is redundant in the model, In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright (C) 2002 John Wiley Sons, Ltd. [less ▲]

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See detailMerits of the multivariate Dale model in genetic association studies
Van Steen, Kristel ULg; Molenberghs, G.; Tahri, N.

in Genetic Epidemiology (2002), 23

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See detailUsing word frequencies for testing equivalence between two DNA sequences
Jansen, I.; Van Steen, Kristel ULg; Molenberghs, G. et al

in Genetic Epidemiology (2002), 23

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See detailSensitivity analysis of longitudinal binary quality of life data with drop-out: an example using the EORTC QLQ-C30
Van Steen, Kristel ULg; Curran, D.; Molenberghs, G.

in Statistics in Medicine (2001), 20(24), 3901-20

Analysing quality of life data (QOL) may be complicated for several reasons. Quality of life data not only involves repeated measures but is also usually collected on ordered categorical responses. In ... [more ▼]

Analysing quality of life data (QOL) may be complicated for several reasons. Quality of life data not only involves repeated measures but 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. In the recent statistical literature, increasing attention is given to methods which can handle non-continuous outcomes in the presence of missing data. The aim of this paper is to investigate the effect on statistical conclusions of applying different modelling techniques to QOL data generated from an EORTC phase III trial. Treatment effects and treatment differences are of major concern. First, a random-effects model is fitted, relating a binary longitudinal response (derived from the physical functioning scale of the QLQ-C30) to several covariates. In a second approach, marginal models are fitted, retaining the response variable and the mean structure used before. The fitted marginal models only differ with respect to the considered estimation procedure: generalized estimating equations (GEE); weighted generalized estimating equations (WGEE), and maximum likelihood (ML). [less ▲]

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See detailAn equivalence test for comparing DNA sequences.
Thijs, H.; Van Steen, Kristel ULg; Molenberghs, G. et al

in American Journal of Human Genetics (2001), 69(4), 1576

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See detailAnalysis of binary data from a psychiatric study: a local influence approach
Jansen, I.; Molenberghs, G.; Aerts, M. et al

in Klein; Korsholm (Eds.) Statistical Modelling. Proceedings of the 16th International Workshop on Statistical Modelling. New Trends in Statistical Modelling. (2001)

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See detailA Local Influence Approach to Sensitivity Analysis of Incomplete Longitudinal Ordinal Data
Van Steen, Kristel ULg; Molenberghs, G.; Verbeke, G. et al

in Statistical Modelling : An International Journal (2001), 1

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See detailIntroduction of the multivariate Dale model in genetic association studies.
Van Steen, Kristel ULg; Tahri, N.; Molenberghs, G.

in American Journal of Human Genetics (2001), 69(4), 1289

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