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Geert Molenberghs - One of the best experts on this subject based on the ideXlab platform.
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Handbook of Missing Data Methodology
2019Co-Authors: Geert Molenberghs, Garrett M. Fitzmaurice, Michael G. Kenward, Anastasios A. Tsiatis, Geert VerbekeAbstract:Preliminaries Introduction and Preliminaries Garrett M. Fitzmaurice, Michael G. Kenward, Geert Molenberghs, Geert Verbeke, and Anastasios A. Tsiatis Developments of Methods and Critique of ad hoc Methods James R. Carpenter and Michael G. Kenward Likelihood and Bayesian Methods Introduction and Overview Michael G. Kenward, Geert Molenberghs, and Geert Verbeke Perspective and Historical Overview Michael G. Kenward and Geert Molenberghs Bayesian Methods Michael J. Daniels and Joseph W. Hogan Joint Modeling of Longitudinal and Time-to-Event Data Dimitris Rizopoulos Semi-Parametric Methods Introduction and Overview Garrett M. Fitzmaurice Missing Data Methods: A Semi-Parametric Perspective Anastasios A. Tsiatis and Marie Davidian Double-Robust Methods Andrea Rotnitzky and Stijn Vansteelandt Pseudo-Likelihood Methods for Incomplete Data Geert Molenberghs and Michael G. Kenward Multiple Imputation Introduction Michael G. Kenward Multiple Imputation: Perspective and Historical Overview John B. Carlin Fully Conditional Specification Stef van Buuren Multilevel Multiple Imputation Harvey Goldstein and James R. Carpenter Sensitivity Analysis Introduction and Overview Geert Molenberghs, Geert Verbeke, and Michael G. Kenward A Likelihood-Based Perspective Geert Verbeke, Geert Molenberghs, and Michael G. Kenward A Semi-Parametric Perspective Stijn Vansteelandt Bayesian Sensitivity Analysis Joseph W. Hogan, Michael J. Daniels, and Liangyuan Hu Sensitivity Analysis with Multiple Imputation James R. Carpenter and Michael G. Kenward The Elicitation and Use of Expert Opinion Ian R. White Special Topics Introduction and Overview Geert Molenberghs Missing Data in Clinical Trials Craig Mallinckrodt Missing Data in Sample Surveys Thomas R. Belin and Juwon Song Model Diagnostics Dimitris Rizopoulos, Geert Molenberghs, and Geert Verbeke Index
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Improved discrimination between normal-tension and primary open-angle glaucoma with advanced vascular examinations - the Leuven Eye Study
Acta ophthalmologica, 2018Co-Authors: João Barbosa-breda, Geert Molenberghs, Vahid Nassiri, Karel Van Keer, Luis Abegão-pinto, Koen Willekens, Evelien Vandewalle, Amândio Rocha-sousa, Ingeborg StalmansAbstract:[Barbosa-Breda, Joao; Van Keer, Karel; Willekens, Koen; Vandewalle, Evelien; Stalmans, Ingeborg] Katholieke Univ Leuven, Dept Neurosci, Res Grp Ophthalmol, Kapucijncnvoer 33,Blok C,Box 7001, B-3000 Leuven, Belgium. [Barbosa-Breda, Joao; Van Keer, Karel; Willekens, Koen; Vandewalle, Evelien; Stalmans, Ingeborg] UZ Leuven, Dept Ophthalmol, Leuven, Belgium. [Barbosa-Breda, Joao; Rocha-Sousa, Amandio] Univ Porto, Fac Med, Ophthalmol Unit, Surg & Physiol, Porto, Portugal. [Abegao-Pinto, Luis] Univ Lisbon, Fac Med, Visual Sci Study Ctr, Lisbon, Portugal. [Nassiri, Vahid; Molenberghs, Geert] Katholieke Univ Leuven, I BioStat, Leuven, Belgium. [Molenberghs, Geert] Hasselt Univ, I BioStat, Hasselt, Belgium.
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Clusters with random size: maximum likelihood versus weighted estimation
Statistica Sinica, 2018Co-Authors: Lisa Hermans, Geert Molenberghs, Michael G. Kenward, Marc Aerts, Vahid Nassiri, Wim Van Der Elst, Geert VerbekeAbstract:Geert Molenberghs, Mike Kenward, Marc Aerts, Geert Verbeke and Wim van der Elst gratefully acknowledge support from IAP research Network P7/06 of the Belgian Government (Belgian Science Policy) and Geert Molenberghs and Geert Verbeke from ExaScience Project.
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Hierarchical models with normal and conjugate random effects : a review
Sort-statistics and Operations Research Transactions, 2017Co-Authors: Geert Molenberghs, Geert Verbeke, Clarice Garcia Borges DemétrioAbstract:Molenberghs, Verbeke, and Demetrio (2007) and Molenberghs et al. (2010) proposed a general framework to model hierarchical data subject to within-unit correlation and/or overdispersion. The framework extends classical overdispersion models as well as generalized linear mixed models. Subsequent work has examined various aspects that lead to the formulation of several extensions. A unified treatment of the model framework and key extensions is provided. Particular extensions discussed are: explicit calculation of correlation and other moment-based functions, joint modelling of several hierarchical sequences, versions with direct marginally interpretable parameters, zero-inflation in the count case, and influence diagnostics. The basic models and several extensions are illustrated using a set of key examples, one per data type (count, binary, multinomial, ordinal, and time-to-event).
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Local influence diagnostics for hierarchical finite-mixture random-effects models.
Biometrical journal. Biometrische Zeitschrift, 2017Co-Authors: Trias Wahyuni Rakhmawati, Geert Molenberghs, Geert Verbeke, Christel FaesAbstract:The main objective of this paper is to evaluate the influence of individual subjects exerted on a random-effects model for repeated measures, where the random effects follow a mixture distribution. The diagnostic tool is based on local influence with perturbation scheme that explicitly targets influences resulting from perturbing the mixture component probabilities. Bruckers, Molenberghs, Verbeke, and Geys (2016) considered a similar model, but focused on influences stemming from perturbing a subject's likelihood contributions as a whole. We also compare the two types of perturbation. Our results are illustrated using linear mixed models fitted to data from three studies. A simulation study is also conducted in order to strengthen the result from case studies.
Geert Verbeke - One of the best experts on this subject based on the ideXlab platform.
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Handbook of Missing Data Methodology
2019Co-Authors: Geert Molenberghs, Garrett M. Fitzmaurice, Michael G. Kenward, Anastasios A. Tsiatis, Geert VerbekeAbstract:Preliminaries Introduction and Preliminaries Garrett M. Fitzmaurice, Michael G. Kenward, Geert Molenberghs, Geert Verbeke, and Anastasios A. Tsiatis Developments of Methods and Critique of ad hoc Methods James R. Carpenter and Michael G. Kenward Likelihood and Bayesian Methods Introduction and Overview Michael G. Kenward, Geert Molenberghs, and Geert Verbeke Perspective and Historical Overview Michael G. Kenward and Geert Molenberghs Bayesian Methods Michael J. Daniels and Joseph W. Hogan Joint Modeling of Longitudinal and Time-to-Event Data Dimitris Rizopoulos Semi-Parametric Methods Introduction and Overview Garrett M. Fitzmaurice Missing Data Methods: A Semi-Parametric Perspective Anastasios A. Tsiatis and Marie Davidian Double-Robust Methods Andrea Rotnitzky and Stijn Vansteelandt Pseudo-Likelihood Methods for Incomplete Data Geert Molenberghs and Michael G. Kenward Multiple Imputation Introduction Michael G. Kenward Multiple Imputation: Perspective and Historical Overview John B. Carlin Fully Conditional Specification Stef van Buuren Multilevel Multiple Imputation Harvey Goldstein and James R. Carpenter Sensitivity Analysis Introduction and Overview Geert Molenberghs, Geert Verbeke, and Michael G. Kenward A Likelihood-Based Perspective Geert Verbeke, Geert Molenberghs, and Michael G. Kenward A Semi-Parametric Perspective Stijn Vansteelandt Bayesian Sensitivity Analysis Joseph W. Hogan, Michael J. Daniels, and Liangyuan Hu Sensitivity Analysis with Multiple Imputation James R. Carpenter and Michael G. Kenward The Elicitation and Use of Expert Opinion Ian R. White Special Topics Introduction and Overview Geert Molenberghs Missing Data in Clinical Trials Craig Mallinckrodt Missing Data in Sample Surveys Thomas R. Belin and Juwon Song Model Diagnostics Dimitris Rizopoulos, Geert Molenberghs, and Geert Verbeke Index
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Clusters with random size: maximum likelihood versus weighted estimation
Statistica Sinica, 2018Co-Authors: Lisa Hermans, Geert Molenberghs, Michael G. Kenward, Marc Aerts, Vahid Nassiri, Wim Van Der Elst, Geert VerbekeAbstract:Geert Molenberghs, Mike Kenward, Marc Aerts, Geert Verbeke and Wim van der Elst gratefully acknowledge support from IAP research Network P7/06 of the Belgian Government (Belgian Science Policy) and Geert Molenberghs and Geert Verbeke from ExaScience Project.
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Local influence diagnostics for hierarchical finite-mixture random-effects models.
Biometrical journal. Biometrische Zeitschrift, 2017Co-Authors: Trias Wahyuni Rakhmawati, Geert Molenberghs, Geert Verbeke, Christel FaesAbstract:The main objective of this paper is to evaluate the influence of individual subjects exerted on a random-effects model for repeated measures, where the random effects follow a mixture distribution. The diagnostic tool is based on local influence with perturbation scheme that explicitly targets influences resulting from perturbing the mixture component probabilities. Bruckers, Molenberghs, Verbeke, and Geys (2016) considered a similar model, but focused on influences stemming from perturbing a subject's likelihood contributions as a whole. We also compare the two types of perturbation. Our results are illustrated using linear mixed models fitted to data from three studies. A simulation study is also conducted in order to strengthen the result from case studies.
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Hierarchical models with normal and conjugate random effects : a review
Sort-statistics and Operations Research Transactions, 2017Co-Authors: Geert Molenberghs, Geert Verbeke, Clarice Garcia Borges DemétrioAbstract:Molenberghs, Verbeke, and Demetrio (2007) and Molenberghs et al. (2010) proposed a general framework to model hierarchical data subject to within-unit correlation and/or overdispersion. The framework extends classical overdispersion models as well as generalized linear mixed models. Subsequent work has examined various aspects that lead to the formulation of several extensions. A unified treatment of the model framework and key extensions is provided. Particular extensions discussed are: explicit calculation of correlation and other moment-based functions, joint modelling of several hierarchical sequences, versions with direct marginally interpretable parameters, zero-inflation in the count case, and influence diagnostics. The basic models and several extensions are illustrated using a set of key examples, one per data type (count, binary, multinomial, ordinal, and time-to-event).
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The analysis of multivariate longitudinal data: A review: Response to letter of M. Gebregziabher
Statistical methods in medical research, 2017Co-Authors: Geert Verbeke, Geert Molenberghs, Steffen Fieuws, Marie DavidianAbstract:[Verbeke, Geert; Fieuws, Steffen; Molenberghs, Geert] Katholieke Univ Leuven, Interuniv Inst Biostat & Stat Bioinformat, B-3000 Leuven, Belgium. [Verbeke, Geert; Molenberghs, Geert] Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat, Diepenbeek, Belgium. [Davidian, Marie] North Carolina State Univ, Dept Stat, Raleigh, NC USA.
Molenberghs Geert - One of the best experts on this subject based on the ideXlab platform.
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local influence diagnostics for hierarchical finite-mixture random effects models
'Informa UK Limited', 2018Co-Authors: Trias Wahyuni Rakhmawati, Molenberghs Geert, Verbeke Geert, Faes CAbstract:The main objective of this paper is to evaluate the influence of individual subjects exerted on a random-effects model for repeated measures, where the random effects follow a mixture distribution. The diagnostic tool is based on local influence with perturbation scheme that explicitly targets influences resulting from perturbing the mixture component probabilities. Bruckers, Molenberghs, Verbeke, and Geys (2016) considered a similar model, but focused on influences stemming from perturbing a subject's likelihood contributions as a whole. We also compare the two types of perturbation. Our results are illustrated using linear mixed models fitted to data from three studies. A simulation study is also conducted in order to strengthen the result from case studies.status: publishe
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CLUSTERS WITH UNEQUAL SIZE: MAXIMUM LIKELIHOOD VERSUS WEIGHTED ESTIMATION IN LARGE SAMPLES
'Institute of Statistical Science', 2018Co-Authors: Hermans Lisa, Molenberghs Geert, Van Der Elst Wim, Nassiri Vahid, Kenward, Michael G, Aerts Marc, Verbeke GeertAbstract:The analysis of hierarchical data that take the form of clusters with random size has received considerable attention. The focus here is on samples that are very large in terms of number of clusters and/or members per cluster, on the one hand, as well as on very small samples (e.g., when studying rare diseases), on the other. Whereas maximum likelihood inference is straightforward in medium to large samples, in samples of sizes considered here it may be prohibitive. We propose sample-splitting (Molenberghs, Verbeke and Iddi (2011)) as a way to replace iterative optimization of a likelihood that does not admit an analytical solution, with closed-form calculations. We use pseudo-likelihood (Molenberghs et al. (2014)), consisting of computing weighted averages over solutions obtained for each cluster size occurring. As a result, the statistical properties of this approach need to be investigated, especially because the minimal sufficient statistics involved are incomplete. The operational characteristics were studied using simulations. Simulations were also done to compare the proposed method to existing techniques developed to circumvent difficulties with unequal cluster sizes, such as multiple imputation. It follows that the proposed non-iterative methods have a strong beneficial impact on computation time; at the same time, the method is the most precise among its competitors considered. The findings are illustrated using data from a developmental toxicity study, where clusters are formed of fetuses within litters.status: publishe
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Hierarchical models with normal and conjugate random effects: a review
Universitat Rovira i Virgili, 2017Co-Authors: Molenberghs Geert, Verbeke Geert, Demétrio, Clarice G.b.Abstract:Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework to model hierarchical data subject to within-unit correlation and/or overdispersion. The framework extends classical overdispersion models as well as generalized linear mixed models. Subsequent work has examined various aspects that lead to the formulation of several extensions. A unified treatment of the model framework and key extensions is provided. Particular extensions discussed are: explicit calculation of correlation and other moment-based functions, joint modelling of several hierarchical sequences, versions with direct marginally interpretable parameters, zero-inflation in the count case, and influence diagnostics. The basic models and several extensions are illustrated using a set of key examples, one per data type (count, binary, multinomial, ordinal, and time-to-event)
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Hierarchical models with normal and conjugate random effects: a review
Institut d'Estadística de Catalunya, 2017Co-Authors: Molenberghs Geert, Verbeke Geert, Demétrio, Clarice G.b.Abstract:Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework to model hierarchical data subject to within-unit correlation and/or overdispersion. The framework extends classical overdispersion models as well as generalized linear mixed models. Subsequent work has examined various aspects that lead to the formulation of several extensions. A unified treatment of the model framework and key extensions is provided. Particular extensions discussed are: explicit calculation of correlation and other moment-based functions, joint modelling of several hierarchical sequences, versions with direct marginally interpretable parameters, zero-inflation in the count case, and influence diagnostics. The basic models and several extensions are illustrated using a set of key examples, one per data type (count, binary, multinomial, ordinal, and time-to-event).Peer Reviewe
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Diagnosing misspecification of the random-effects distribution in mixed models
'Wiley', 2017Co-Authors: Drikvandi R, Verbeke Geert, Molenberghs GeertAbstract:It is traditionally assumed that the random effects in mixed models follow a multivariate normal distribution, making likelihood-based inferences more feasible theoretically and computationally. However, this assumption does not necessarily hold in practice which may lead to biased and unreliable results. We introduce a novel diagnostic test based on the so-called gradient function proposed by Verbeke and Molenberghs (2013) to assess the random-effects distribution. We establish asymptotic properties of our test and show that, under a correctly specified model, the proposed test statistic converges to a weighted sum of independent chi-squared random variables each with one degree of freedom. The weights, which are eigenvalues of a square matrix, can be easily calculated. We also develop a parametric bootstrap algorithm for small samples. Our strategy can be used to check the adequacy of any distribution for random effects in a wide class of mixed models, including linear mixed models, generalized linear mixed models, and non-linear mixed models, with univariate as well as multivariate random effects. Both asymptotic and bootstrap proposals are evaluated via simulations and a real data analysis of a randomized multicenter study on toenail dermatophyte onychomycosis.status: publishe
Verbeke Geert - One of the best experts on this subject based on the ideXlab platform.
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local influence diagnostics for hierarchical finite-mixture random effects models
'Informa UK Limited', 2018Co-Authors: Trias Wahyuni Rakhmawati, Molenberghs Geert, Verbeke Geert, Faes CAbstract:The main objective of this paper is to evaluate the influence of individual subjects exerted on a random-effects model for repeated measures, where the random effects follow a mixture distribution. The diagnostic tool is based on local influence with perturbation scheme that explicitly targets influences resulting from perturbing the mixture component probabilities. Bruckers, Molenberghs, Verbeke, and Geys (2016) considered a similar model, but focused on influences stemming from perturbing a subject's likelihood contributions as a whole. We also compare the two types of perturbation. Our results are illustrated using linear mixed models fitted to data from three studies. A simulation study is also conducted in order to strengthen the result from case studies.status: publishe
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CLUSTERS WITH UNEQUAL SIZE: MAXIMUM LIKELIHOOD VERSUS WEIGHTED ESTIMATION IN LARGE SAMPLES
'Institute of Statistical Science', 2018Co-Authors: Hermans Lisa, Molenberghs Geert, Van Der Elst Wim, Nassiri Vahid, Kenward, Michael G, Aerts Marc, Verbeke GeertAbstract:The analysis of hierarchical data that take the form of clusters with random size has received considerable attention. The focus here is on samples that are very large in terms of number of clusters and/or members per cluster, on the one hand, as well as on very small samples (e.g., when studying rare diseases), on the other. Whereas maximum likelihood inference is straightforward in medium to large samples, in samples of sizes considered here it may be prohibitive. We propose sample-splitting (Molenberghs, Verbeke and Iddi (2011)) as a way to replace iterative optimization of a likelihood that does not admit an analytical solution, with closed-form calculations. We use pseudo-likelihood (Molenberghs et al. (2014)), consisting of computing weighted averages over solutions obtained for each cluster size occurring. As a result, the statistical properties of this approach need to be investigated, especially because the minimal sufficient statistics involved are incomplete. The operational characteristics were studied using simulations. Simulations were also done to compare the proposed method to existing techniques developed to circumvent difficulties with unequal cluster sizes, such as multiple imputation. It follows that the proposed non-iterative methods have a strong beneficial impact on computation time; at the same time, the method is the most precise among its competitors considered. The findings are illustrated using data from a developmental toxicity study, where clusters are formed of fetuses within litters.status: publishe
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Hierarchical models with normal and conjugate random effects: a review
Universitat Rovira i Virgili, 2017Co-Authors: Molenberghs Geert, Verbeke Geert, Demétrio, Clarice G.b.Abstract:Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework to model hierarchical data subject to within-unit correlation and/or overdispersion. The framework extends classical overdispersion models as well as generalized linear mixed models. Subsequent work has examined various aspects that lead to the formulation of several extensions. A unified treatment of the model framework and key extensions is provided. Particular extensions discussed are: explicit calculation of correlation and other moment-based functions, joint modelling of several hierarchical sequences, versions with direct marginally interpretable parameters, zero-inflation in the count case, and influence diagnostics. The basic models and several extensions are illustrated using a set of key examples, one per data type (count, binary, multinomial, ordinal, and time-to-event)
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Hierarchical models with normal and conjugate random effects: a review
Institut d'Estadística de Catalunya, 2017Co-Authors: Molenberghs Geert, Verbeke Geert, Demétrio, Clarice G.b.Abstract:Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework to model hierarchical data subject to within-unit correlation and/or overdispersion. The framework extends classical overdispersion models as well as generalized linear mixed models. Subsequent work has examined various aspects that lead to the formulation of several extensions. A unified treatment of the model framework and key extensions is provided. Particular extensions discussed are: explicit calculation of correlation and other moment-based functions, joint modelling of several hierarchical sequences, versions with direct marginally interpretable parameters, zero-inflation in the count case, and influence diagnostics. The basic models and several extensions are illustrated using a set of key examples, one per data type (count, binary, multinomial, ordinal, and time-to-event).Peer Reviewe
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Diagnosing misspecification of the random-effects distribution in mixed models
'Wiley', 2017Co-Authors: Drikvandi R, Verbeke Geert, Molenberghs GeertAbstract:It is traditionally assumed that the random effects in mixed models follow a multivariate normal distribution, making likelihood-based inferences more feasible theoretically and computationally. However, this assumption does not necessarily hold in practice which may lead to biased and unreliable results. We introduce a novel diagnostic test based on the so-called gradient function proposed by Verbeke and Molenberghs (2013) to assess the random-effects distribution. We establish asymptotic properties of our test and show that, under a correctly specified model, the proposed test statistic converges to a weighted sum of independent chi-squared random variables each with one degree of freedom. The weights, which are eigenvalues of a square matrix, can be easily calculated. We also develop a parametric bootstrap algorithm for small samples. Our strategy can be used to check the adequacy of any distribution for random effects in a wide class of mixed models, including linear mixed models, generalized linear mixed models, and non-linear mixed models, with univariate as well as multivariate random effects. Both asymptotic and bootstrap proposals are evaluated via simulations and a real data analysis of a randomized multicenter study on toenail dermatophyte onychomycosis.status: publishe
Michael G. Kenward - One of the best experts on this subject based on the ideXlab platform.
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Handbook of Missing Data Methodology
2019Co-Authors: Geert Molenberghs, Garrett M. Fitzmaurice, Michael G. Kenward, Anastasios A. Tsiatis, Geert VerbekeAbstract:Preliminaries Introduction and Preliminaries Garrett M. Fitzmaurice, Michael G. Kenward, Geert Molenberghs, Geert Verbeke, and Anastasios A. Tsiatis Developments of Methods and Critique of ad hoc Methods James R. Carpenter and Michael G. Kenward Likelihood and Bayesian Methods Introduction and Overview Michael G. Kenward, Geert Molenberghs, and Geert Verbeke Perspective and Historical Overview Michael G. Kenward and Geert Molenberghs Bayesian Methods Michael J. Daniels and Joseph W. Hogan Joint Modeling of Longitudinal and Time-to-Event Data Dimitris Rizopoulos Semi-Parametric Methods Introduction and Overview Garrett M. Fitzmaurice Missing Data Methods: A Semi-Parametric Perspective Anastasios A. Tsiatis and Marie Davidian Double-Robust Methods Andrea Rotnitzky and Stijn Vansteelandt Pseudo-Likelihood Methods for Incomplete Data Geert Molenberghs and Michael G. Kenward Multiple Imputation Introduction Michael G. Kenward Multiple Imputation: Perspective and Historical Overview John B. Carlin Fully Conditional Specification Stef van Buuren Multilevel Multiple Imputation Harvey Goldstein and James R. Carpenter Sensitivity Analysis Introduction and Overview Geert Molenberghs, Geert Verbeke, and Michael G. Kenward A Likelihood-Based Perspective Geert Verbeke, Geert Molenberghs, and Michael G. Kenward A Semi-Parametric Perspective Stijn Vansteelandt Bayesian Sensitivity Analysis Joseph W. Hogan, Michael J. Daniels, and Liangyuan Hu Sensitivity Analysis with Multiple Imputation James R. Carpenter and Michael G. Kenward The Elicitation and Use of Expert Opinion Ian R. White Special Topics Introduction and Overview Geert Molenberghs Missing Data in Clinical Trials Craig Mallinckrodt Missing Data in Sample Surveys Thomas R. Belin and Juwon Song Model Diagnostics Dimitris Rizopoulos, Geert Molenberghs, and Geert Verbeke Index
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Clusters with random size: maximum likelihood versus weighted estimation
Statistica Sinica, 2018Co-Authors: Lisa Hermans, Geert Molenberghs, Michael G. Kenward, Marc Aerts, Vahid Nassiri, Wim Van Der Elst, Geert VerbekeAbstract:Geert Molenberghs, Mike Kenward, Marc Aerts, Geert Verbeke and Wim van der Elst gratefully acknowledge support from IAP research Network P7/06 of the Belgian Government (Belgian Science Policy) and Geert Molenberghs and Geert Verbeke from ExaScience Project.
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Unbalanced cluster sizes and rates of convergence in mixed-effects models for clustered data
Journal of Statistical Computation and Simulation, 2015Co-Authors: W. Van Der Elst, Geert Verbeke, Michael G. Kenward, Vahid Nassiri, Lisa Hermans, Geert MolenberghsAbstract:ABSTRACTConvergence problems often arise when complex linear mixed-effects models are fitted. Previous simulation studies (see, e.g. [Buyse M, Molenberghs G, Burzykowski T, Renard D, Geys H. The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics. 2000;1:49–67, Renard D, Geys H, Molenberghs G, Burzykowski T, Buyse M. Validation of surrogate endpoints in multiple randomized clinical trials with discrete outcomes. Biom J. 2002;44:921–935]) have shown that model convergence rates were higher (i) when the number of available clusters in the data increased, and (ii) when the size of the between-cluster variability increased (relative to the size of the residual variability). The aim of the present simulation study is to further extend these findings by examining the effect of an additional factor that is hypothesized to affect model convergence, i.e. imbalance in cluster size. The results showed that divergence rates were substantially higher for data sets with unbalance...
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Missing data in clinical trials: a data interpretation problem with statistical solutions?
Clinical Investigation, 2012Co-Authors: Michael G. KenwardAbstract:Mike Kenward speaks to Laura Harvey, Assistant Commissioning Editor. Mike Kenward has been GlaxoSmithKline Professor of Biostatistics at the London School of Hygiene since 1999, with former positions at the Universities of Kent and Reading in the UK and research institutes in the UK, Iceland and Finland. His main research interests are in the analysis of longitudinal data, crossover trials, small sample inference in restricted maximum likelihood and the problem of missing data. He has coauthored three text books including ‘The Design and Analysis of Cross-Over Trials’ with Byron Jones and ‘Missing Data in Clinical Studies’ with Geert Molenberghs. He was formally a coeditor of Biometrics, and is currently an associate editor of Biostatistics. Over the last 25 years he has acted as a consultant in biostatistics, largely for the pharmaceutical industry, and he has given over 100 short courses worldwide on various topics in biostatistics, and has appeared as an expert witness in the US Federal District Court....
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Generalized shared-parameter models and missingness at random:
Statistical Modelling, 2011Co-Authors: An Creemers, Geert Molenberghs, Geert Verbeke, Marc Aerts, Niel Hens, Michael G. KenwardAbstract:When data are incomplete, models are often catalogued according to one of the three modelling frameworks to which they belong: selection models (SeM), pattern-mixture models (PMM) and shared-parameter models (SPM). The missing data mechanism is conventionally classified as missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Under MCAR, measurement and missingness mechanism are independent, but that is not the case for MAR. The definition of MAR is in SeM terms. Molenberghs et al. (1998) provided a characterization for PMM. Here, MAR is characterized in the SPM framework, using an extended SPM class. A subfamily, satisfying the MAR condition, is studied in detail. Particular implications for non-monotone missingness as well as for longitudinal data subject to dropout are studied. It is indicated how SPM can be constrained such that dropout at a given point in time can depend on current and past, but not on future measurements. Although, a natural requirement, it i...