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1,130 results on '"Verbeke, Geert"'

8. An observational cohort study of histological screening for BK polyomavirus nephropathy following viral replication in plasma

Author

Cleenders, Evert, Koshy, Priyanka, Van Loon, Elisabet, Lagrou, Katrien, Beuselinck, Kurt, Andrei, Graciela, Crespo, Marta, De Vusser, Katrien, Kuypers, Dirk, Lerut, Evelyne, Mertens, Kris, Mineeva-Sangwo, Olga, Randhawa, Parmjeet, Senev, Aleksandar, Snoeck, Robert, Sprangers, Ben, Tinel, Claire, Van Craenenbroeck, Amaryllis, van den Brand, Jan, Van Ranst, Marc, Verbeke, Geert, Coemans, Maarten, and Naesens, Maarten

Published
2023
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9. COVID-19

Author

Molenberghs, Geert, primary, Neyens, Thomas, additional, and Verbeke, Geert, additional

Published
2023
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13. Missing Data

Author

Molenberghs, Geert, primary, Beunckens, Caroline, additional, Jansen, Ivy, additional, Thijs, Herbert, additional, Verbeke, Geert, additional, and Kenward, Michael G., additional

Published
2023
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16. Modulation of thromboinflammation in hospitalized COVID‐19 patients with aprotinin, low molecular weight heparin, and anakinra: The DAWn‐Antico study

Author

Engelen, Matthias M., Van Thillo, Quentin, Betrains, Albrecht, Gyselinck, Iwein, Martens, Caroline P., Spalart, Valérie, Ockerman, Anna, Devooght, Caroline, Wauters, Joost, Gunst, Jan, Wouters, Carine, Vandenbriele, Christophe, Rex, Steffen, Liesenborghs, Laurens, Wilmer, Alexander, Meersseman, Philippe, Van den Berghe, Greet, Dauwe, Dieter, Belmans, Ann, Thomeer, Michiel, Fivez, Tom, Mesotten, Dieter, Ruttens, David, Heytens, Luc, Dapper, Ilse, Tuyls, Sebastiaan, De Tavernier, Brecht, Verhamme, Peter, Vanassche, Thomas, Van Wijngaerden, Eric, Janssens, Wim, Meyfroidt, Geert, Vos, Robin, Devos, Timothy, De Munter, Paul, Neyts, Johan, Dupont, Lieven, Spriet, Isabel, Verbeke, Geert, Claes, Kathleen, Robberecht, Wim, Van Geet, Chris, Debaveye, Barbara, Ceunen, Helga, Servaes, Veerle, Cludts, Katrien, Vanheule, Kristine, Jacobs, Cato, Roebroek, Daimy, Dreesen, Paulien, Smet, Nele, Dolhain, Jan, Hoppenbrouwers, Mieke, Wens, Kathleen, Daems, Kristel, and D’hondt, Monique

Published
2022
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18. A combined overdispersed longitudinal model for nominal data.

Author

Sercundes, Ricardo K., Molenberghs, Geert, Verbeke, Geert, Demétrio, Clarice G.B., da Silva, Sila C., and Moral, Rafael A.

Subjects
BETA distribution, MULTINOMIAL distribution, RANDOM sets, LONGITUDINAL method, SAMPLE size (Statistics)
Abstract

Longitudinal studies involving nominal outcomes are carried out in various scientific areas. These outcomes are frequently modelled using a generalized linear mixed modelling (GLMM) framework. This widely used approach allows for the modelling of the hierarchy in the data to accommodate different degrees of overdispersion. In this article, a combined model (CM) that takes into account overdispersion and clustering through two separate sets of random effects is formulated. Maximum likelihood estimation with analytic-numerical integration is used to estimate the model parameters. To examine the relative performance of the CM and the GLMM, simulation studies were carried out, exploring scenarios with different sample sizes, types of random effects, and overdispersion. Both models were applied to a real dataset obtained from an experiment in agriculture. We also provide an implementation of these models through SAS code. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Smoking, Obesity, and Metabolic Syndrome in Two High Security Settings.

Author

Jeandarme, Inge, Goktas, Gokhan, Claessens, Barbara, Michem, Tine, Van Poucke, Sofie, and Verbeke, Geert

Abstract

Forensic psychiatric patients pose a vulnerable population due to their unbalanced diet, overweight, smoking, adverse effects of medication, and sedentary lifestyle within secure settings. These lifestyle traits have been correlated with a higher incidence of morbidity and a lower life expectancy. Nonetheless, empirical data regarding these topics remains limited. In the current study we examined obesity, metabolic syndrome and smoking patterns in two high-security settings in Flanders. We also examined the influence of psychiatric diagnoses and the usage of antipsychotic medication to these characteristics. Results indicated that obesity, smoking, and metabolic syndrome were highly prevalent across different diagnoses. In contrast to previous findings, there was no association between the presence of metabolic syndrome and the use of (second generation) antipsychotics. Missing information in the electronic files further indicated that there remains room for improvement in the detection and monitoring of physical health. It is recommended that forensic institutions pay more attention to the provision of care programs to manage weight and smoking. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Itraconazole for COVID-19: preclinical studies and a proof-of-concept randomized clinical trial

Author

Liesenborghs, Laurens, Spriet, Isabel, Jochmans, Dirk, Belmans, Ann, Gyselinck, Iwein, Teuwen, Laure-Anne, ter Horst, Sebastiaan, Dreesen, Erwin, Geukens, Tatjana, Engelen, Matthias M., Landeloos, Ewout, Geldhof, Vincent, Ceunen, Helga, Debaveye, Barbara, Vandenberk, Bert, Van der Linden, Lorenz, Jacobs, Sofie, Langendries, Lana, Boudewijns, Robbert, Do, Thuc Nguyen Dan, Chiu, Winston, Wang, Xinyu, Zhang, Xin, Weynand, Birgit, Vanassche, Thomas, Devos, Timothy, Meyfroidt, Geert, Janssens, Wim, Vos, Robin, Vermeersch, Pieter, Wauters, Joost, Verbeke, Geert, De Munter, Paul, Kaptein, Suzanne J.F., Rocha-Pereira, Joana, Delang, Leen, Van Wijngaerden, Eric, Neyts, Johan, and Verhamme, Peter

Published
2021
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21. Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country

Author

Molenberghs, Geert, Buyse, Marc, Abrams, Steven, Hens, Niel, Beutels, Philippe, Faes, Christel, Verbeke, Geert, Van Damme, Pierre, Goossens, Herman, Neyens, Thomas, Herzog, Sereina, Theeten, Heidi, Pepermans, Koen, Abad, Ariel Alonso, Van Keilegom, Ingrid, Speybroeck, Niko, Legrand, Catherine, De Buyser, Stefanie, and Hulstaert, Frank

Published
2020
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23. Mixed Models with Emphasis on Large Data Sets

Author

Verbeke, Geert, Molenberghs, Geert, Fieuws, Steffen, Iddi, Samuel, DeFanti, Thomas, Series editor, Grafton, Anthony, Series editor, Levy, Thomas E., Series editor, Manovich, Lev, Series editor, Rockwood, Alyn, Series editor, Speelman, Dirk, editor, Heylen, Kris, editor, and Geeraerts, Dirk, editor

Published
2018
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26. A Family of Generalized Linear Models for Repeated Measures with Normal and Conjugate Random Effects

Author

Molenberghs, Geert, Verbeke, Geert, Demétrio, Clarice G. B., and Vieira, Afrânio M. C.

Subjects
Statistics - Methodology
Abstract

Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious members are the Bernoulli model for binary data, leading to logistic regression, and the Poisson model for count data, leading to Poisson regression. Two of the main reasons for extending this family are (1) the occurrence of overdispersion, meaning that the variability in the data is not adequately described by the models, which often exhibit a prescribed mean--variance link, and (2) the accommodation of hierarchical structure in the data, stemming from clustering in the data which, in turn, may result from repeatedly measuring the outcome, for various members of the same family, etc. The first issue is dealt with through a variety of overdispersion models, such as, for example, the beta-binomial model for grouped binary data and the negative-binomial model for counts. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. While both of these phenomena may occur simultaneously, models combining them are uncommon. This paper proposes a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. We place particular emphasis on so-called conjugate random effects at the level of the mean for the first aspect and normal random effects embedded within the linear predictor for the second aspect, even though our family is more general. The binary, count and time-to-event cases are given particular emphasis. Apart from model formulation, we present an overview of estimation methods, and then settle for maximum likelihood estimation with analytic--numerical integration. Implications for the derivation of marginal correlations functions are discussed. The methodology is applied to data from a study in epileptic seizures, a clinical trial in toenail infection named onychomycosis and survival data in children with asthma., Comment: Published in at http://dx.doi.org/10.1214/10-STS328 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2011
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27. Discussion of Likelihood Inference for Models with Unobservables: Another View

Author

Molenberghs, Geert, Kenward, Michael G., and Verbeke, Geert

Subjects
Statistics - Methodology
Abstract

Discussion of "Likelihood Inference for Models with Unobservables: Another View" by Youngjo Lee and John A. Nelder [arXiv:1010.0303], Comment: Published in at http://dx.doi.org/10.1214/09-STS277B the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2010
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28. Trends in cardiovascular disease incidence among 22 million people in the UK over 20 years: population based study.

Author

Conrad, Nathalie, Molenberghs, Geert, Verbeke, Geert, Zaccardi, Francesco, Lawson, Claire, Friday, Jocelyn M., Su, Huimin, Jhund, Pardeep S., Sattar, Naveed, Rahimi, Kazem, Cleland, John G., Khunti, Kamlesh, Budts, Werner, and McMurray, John J. V.

Subjects
AORTIC aneurysms, CARDIOVASCULAR diseases, RESEARCH funding, MYOCARDIAL ischemia, SEX distribution, PERIPHERAL vascular diseases, VEINS, AGE distribution, POPULATION geography, HEART failure, DESCRIPTIVE statistics, ACUTE coronary syndrome, CHRONIC diseases, ODDS ratio, AORTIC stenosis, ATRIAL fibrillation, THROMBOEMBOLISM, ATRIAL flutter, HEART block, STROKE, CONFIDENCE intervals, SOCIAL classes, DISEASE incidence, REGRESSION analysis
Published
2024
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29. A joint penalized spline smoothing model for the number of positive and negative COVID-19 tests.

Author

De Witte, Dries, Abad, Ariel Alonso, Neyens, Thomas, Verbeke, Geert, and Molenberghs, Geert

Subjects
COVID-19 testing, SPLINES, SARS-CoV-2, VIRAL transmission, HETEROSCEDASTICITY
Abstract

One of the key tools to understand and reduce the spread of the SARS-CoV-2 virus is testing. The total number of tests, the number of positive tests, the number of negative tests, and the positivity rate are interconnected indicators and vary with time. To better understand the relationship between these indicators, against the background of an evolving pandemic, the association between the number of positive tests and the number of negative tests is studied using a joint modeling approach. All countries in the European Union, Switzerland, the United Kingdom, and Norway are included in the analysis. We propose a joint penalized spline model in which the penalized spline is reparameterized as a linear mixed model. The model allows for flexible trajectories by smoothing the country-specific deviations from the overall penalized spline and accounts for heteroscedasticity by allowing the autocorrelation parameters and residual variances to vary among countries. The association between the number of positive tests and the number of negative tests is derived from the joint distribution for the random intercepts and slopes. The correlation between the random intercepts and the correlation between the random slopes were both positive. This suggests that, when countries increase their testing capacity, both the number of positive tests and negative tests will increase. A significant correlation was found between the random intercepts, but the correlation between the random slopes was not significant due to a wide credible interval. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Evaluation of a flexible piecewise linear mixed‐effects model in the analysis of randomized cross‐over trials.

Author

Mwangi, Moses, Verbeke, Geert, Njagi, Edmund Njeru, Florez, Alvaro Jose, Mwalili, Samuel, Ivanova, Anna, Bukania, Zipporah N., and Molenberghs, Geert

Subjects
*CROSSOVER trials, *IODINE deficiency, *DIASTOLIC blood pressure, *CLINICAL trials, *RANDOM matrices, *RANDOM numbers
Abstract

Cross‐over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment. They have received a lot of attention, particularly in connection with regulatory requirements for new drugs. The main advantage of using cross‐over designs over conventional parallel designs is increased precision, thanks to within‐subject comparisons. In the statistical literature, more recent developments are discussed in the analysis of cross‐over trials, in particular regarding repeated measures. A piecewise linear model within the framework of mixed effects has been proposed in the analysis of cross‐over trials. In this article, we report on a simulation study comparing performance of a piecewise linear mixed‐effects (PLME) model against two commonly cited models—Grizzle's mixed‐effects (GME) and Jones & Kenward's mixed‐effects (JKME) models—used in the analysis of cross‐over trials. Our simulation study tried to mirror real‐life situation by deriving true underlying parameters from empirical data. The findings from real‐life data confirmed the original hypothesis that high‐dose iodine salt have significantly lowering effect on diastolic blood pressure (DBP). We further sought to evaluate the performance of PLME model against GME and JKME models, within univariate modeling framework through a simulation study mimicking a 2 × 2 cross‐over design. The fixed‐effects, random‐effects and residual error parameters used in the simulation process were estimated from DBP data, using a PLME model. The initial results with full specification of random intercept and slope(s), showed that the univariate PLME model performed better than the GME and JKME models in estimation of variance–covariance matrix (G) governing the random effects, allowing satisfactory model convergence during estimation. When a hierarchical view‐point is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance–covariance matrix of the random effects must be positive‐definite. The PLME model is preferred especially in modeling an increased number of random effects, compared to the GME and JKME models that work equally well with random intercepts only. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Formal and Informal Model Selection with Incomplete Data

Author

Verbeke, Geert, Molenberghs, Geert, and Beunckens, Caroline

Subjects
Statistics - Methodology
Abstract

Model selection and assessment with incomplete data pose challenges in addition to the ones encountered with complete data. There are two main reasons for this. First, many models describe characteristics of the complete data, in spite of the fact that only an incomplete subset is observed. Direct comparison between model and data is then less than straightforward. Second, many commonly used models are more sensitive to assumptions than in the complete-data situation and some of their properties vanish when they are fitted to incomplete, unbalanced data. These and other issues are brought forward using two key examples, one of a continuous and one of a categorical nature. We argue that model assessment ought to consist of two parts: (i) assessment of a model's fit to the observed data and (ii) assessment of the sensitivity of inferences to unverifiable assumptions, that is, to how a model described the unobserved data given the observed ones., Comment: Published in at http://dx.doi.org/10.1214/07-STS253 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2008
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37. Analyzing Incomplete Discrete Longitudinal Clinical Trial Data

Author

Jansen, Ivy, Beunckens, Caroline, Molenberghs, Geert, Verbeke, Geert, and Mallinckrodt, Craig

Subjects
Mathematics - Statistics Theory
Abstract

Commonly used methods to analyze incomplete longitudinal clinical trial data include complete case analysis (CC) and last observation carried forward (LOCF). However, such methods rest on strong assumptions, including missing completely at random (MCAR) for CC and unchanging profile after dropout for LOCF. Such assumptions are too strong to generally hold. Over the last decades, a number of full longitudinal data analysis methods have become available, such as the linear mixed model for Gaussian outcomes, that are valid under the much weaker missing at random (MAR) assumption. Such a method is useful, even if the scientific question is in terms of a single time point, for example, the last planned measurement occasion, and it is generally consistent with the intention-to-treat principle. The validity of such a method rests on the use of maximum likelihood, under which the missing data mechanism is ignorable as soon as it is MAR. In this paper, we will focus on non-Gaussian outcomes, such as binary, categorical or count data. This setting is less straightforward since there is no unambiguous counterpart to the linear mixed model. We first provide an overview of the various modeling frameworks for non-Gaussian longitudinal data, and subsequently focus on generalized linear mixed-effects models, on the one hand, of which the parameters can be estimated using full likelihood, and on generalized estimating equations, on the other hand, which is a nonlikelihood method and hence requires a modification to be valid under MAR. We briefly comment on the position of models that assume missingness not at random and argue they are most useful to perform sensitivity analysis. Our developments are underscored using data from two studies. While the case studies feature binary outcomes, the methodology applies equally well to other discrete-data settings, hence the qualifier ``discrete'' in the title., Comment: Published at http://dx.doi.org/10.1214/088342305000000322 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2006
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39. Targeted HIV Screening in Eight Emergency Departments: The DICI-VIH Cluster-Randomized Two-Period Crossover Trial

Author

Duchêne, Gaëlle, Jegou, Carole, Adnet, Frédéric, Bouchaud, Olivier, Alloui, Chakib, Jauneau, Christine, Troisvallets, Didier, Vandemeulebroucke, Eric, Fossoux, Nadia, Raphaël, Maurice, Vincent-Cassy, Christophe, Quertainmont, Yann, Goujard, Cécile, Pallier, Coralie, Vega, Aglavène, Renaud, Bertrand, Salmon, Dominique, Krivine, Anne, Bastide, Théophile, Galichon, Bertrand, Plaisance, Patrick, Morgand, Marjolaine, Maylin, Sarah, Bercot, Béatrice, Campa, Pauline, Valin, Nadia, Boukli, Narjis, Morand-Joubert, Laurence, Verbrugghe, Rachel, Dautheville, Sandrine, Ray, Patrick, Lebrette, Marie-Gisèle, Amiel, Corinne, Lancien, Cécile, Doumenc, Benoit, Khuong, Marie-Aude, Gros, Isabelle, Mutuon, Pierre, d’Almeida, Kayigan, Fromentin, Hélène, Martin, Maria, Cossé, Charlotte, Burnet, Espérie, Leblanc, Judith, Hejblum, Gilles, Costagliola, Dominique, Durand-Zaleski, Isabelle, Lert, France, de Truchis, Pierre, Verbeke, Geert, Rousseau, Alexandra, Piquet, Hélène, Simon, François, Pateron, Dominique, Simon, Tabassome, and Crémieux, Anne-Claude

Published
2018
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