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Search Results: 1 - 10 of 530 matches for " Geert Verbeke "
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Formal and Informal Model Selection with Incomplete Data
Geert Verbeke,Geert Molenberghs,Caroline Beunckens
Statistics , 2008, DOI: 10.1214/07-STS253
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.
Discussion of Likelihood Inference for Models with Unobservables: Another View
Geert Molenberghs,Michael G. Kenward,Geert Verbeke
Statistics , 2010, DOI: 10.1214/09-STS277B
Abstract: Discussion of "Likelihood Inference for Models with Unobservables: Another View" by Youngjo Lee and John A. Nelder [arXiv:1010.0303]
Modeling overdispersed longitudinal binary data using a combined beta and normal random-effects model
Wondwosen Kassahun, Thomas Neyens, Geert Molenberghs, Christel Faes, Geert Verbeke
Archives of Public Health , 2012, DOI: 10.1186/0778-7367-70-7
Abstract: Two longitudinal binary data sets, collected in south western Ethiopia: the Jimma infant growth study, where the child’s early growth is studied, and the Jimma longitudinal family survey of youth where the adolescent’s school attendance is studied over time, are considered. A new model which combines both overdispersion, and correlation simultaneously, also known as the combined model is applied. In addition, the commonly used methods for binary and binomial data, such as the simple logistic, which accounts neither for the overdispersion nor the correlation, the beta-binomial model, and the logistic-normal model, which accommodate only for the overdispersion, and correlation, respectively, are also considered for comparison purpose. As an alternative estimation technique, a Bayesian implementation of the combined model is also presented.The combined model results in model improvement in fit, and hence the preferred one, based on likelihood comparison, and DIC criterion. Further, the two estimation approaches result in fairly similar parameter estimates and inferences in both of our case studies. Early initiation of breastfeeding has a protective effect against the risk of overweight in late infancy (p?=?0.001), while proportion of overweight seems to be invariant among males and females overtime (p?=?0.66). Gender is significantly associated with school attendance, where girls have a lower rate of attendance (p?=?0.001) as compared to boys.We applied a flexible modeling framework to analyze binary and binomial longitudinal data. Instead of accounting for overdispersion, and correlation separately, both can be accommodated simultaneously, by allowing two separate sets of the beta, and the normal random effects at once.
Analyzing Incomplete Discrete Longitudinal Clinical Trial Data
Ivy Jansen,Caroline Beunckens,Geert Molenberghs,Geert Verbeke,Craig Mallinckrodt
Mathematics , 2006, DOI: 10.1214/088342305000000322
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.
A Family of Generalized Linear Models for Repeated Measures with Normal and Conjugate Random Effects
Geert Molenberghs,Geert Verbeke,Clarice G. B. Demétrio,Afranio M. C. Vieira
Statistics , 2011, DOI: 10.1214/10-STS328
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.
A cluster randomized trial to improve adherence to evidence-based guidelines on diabetes and reduce clinical inertia in primary care physicians in Belgium: study protocol [NTR 1369]
Liesbeth Borgermans, Geert Goderis, Carine Broeke, Chantal Mathieu, Bert Aertgeerts, Geert Verbeke, An Carbonez, Anna Ivanova, Richard Grol, Jan Heyrman
Implementation Science , 2008, DOI: 10.1186/1748-5908-3-42
Abstract: To evaluate interventions to improve adherence to evidence-based guidelines for diabetes and reduce clinical inertia in primary care physicians.Two-arm cluster randomized controlled trial.Primary care physicians in Belgium.Primary care physicians will be randomly allocated to 'Usual' (UQIP) or 'Advanced' (AQIP) Quality Improvement Programs. Physicians in the UQIP will receive interventions addressing the main physician, patient, and office system factors that contribute to clinical inertia. Physicians in the AQIP will receive additional interventions that focus on sustainable behavior changes in patients and providers.Primary endpoints are the proportions of patients within targets for three clinical outcomes: 1) glycosylated hemoglobin < 7%; 2) systolic blood pressure differences ≤130 mmHg; and 3) low density lipoprotein/cholesterol < 100 mg/dl. Secondary endpoints are individual improvements in 12 validated parameters: glycosylated hemoglobin, low and high density lipoprotein/cholesterol, total cholesterol, systolic blood pressure, diastolic blood pressure, weight, physical exercise, healthy diet, smoking status, and statin and anti-platelet therapy.Statistical analyses will be performed using an intent-to-treat approach with a multilevel model. Linear and generalized linear mixed models will be used to account for the clustered nature of the data, i.e., patients clustered withinimary care physicians, and repeated assessments clustered within patients. To compare patient characteristics at baseline and between the intervention arms, the generalized estimating equations (GEE) approach will be used, taking the clustered nature of the data within physicians into account. We will also use the GEE approach to test for differences in evolution of the primary and secondary endpoints for all patients, and for patients in the two interventions arms, accounting for within-patient clustering.number: NTR 1369.Diabetes management is a complex process requiring physiological, p
Physical characteristics of the back are not predictive of low back pain in healthy workers: A prospective study
An Van Nieuwenhuyse, Geert Crombez, Alex Burdorf, Geert Verbeke, Raphael Masschelein, Guido Moens, Philippe Mairiaux, the BelCoBack Study Group
BMC Musculoskeletal Disorders , 2009, DOI: 10.1186/1471-2474-10-2
Abstract: This study is part of the Belgian Low Back Cohort (BelCoBack) Study, a prospective study to identify risk factors for the development of low back disorders in occupational settings. The study population for this paper were 692 young healthcare or distribution workers (mean age of 26 years) with no or limited back antecedents in the year before inclusion. At baseline, these workers underwent a standardised physical examination of the low back. One year later, they completed a questionnaire on the occurrence of LBP and some of its characteristics. To study the respective role of predictors at baseline on the occurrence of LBP, we opted for Cox regression with a constant risk period. Analyses were performed separately for workers without any back antecedents in the year before inclusion ('asymptomatic' workers) and for workers with limited back antecedents in the year before inclusion ('mildly symptomatic' workers).In the group of 'asymptomatic' workers, obese workers showed a more than twofold-increased risk on the development of LBP as compared to non-obese colleagues (RR 2.57, 95%CI: 1.09 – 6.09). In the group of 'mildly symptomatic' workers, the self-reports of pain before the examination turned out to be most predictive (RR 3.89, 95%CI: 1.20 – 12.64).This study showed that, in a population of young workers wh no or limited antecedents of LBP at baseline, physical examinations, as routinely assessed in occupational medicine, are not useful to predict workers at risk for the development of back disorders one year later.Low back pain (LBP) is a prevalent health problem that imposes an enormous burden on individuals and society [1]. Work-related factors such as bending and twisting, whole-body vibration, manual materials handling and individual variables such as history of pain and age have been consistently associated with the occurrence of low back pain in diverse settings, including occupational ones [2]. At the work place, prevention has therefore focused upon the
Evidence for Co-Evolution between Human MicroRNAs and Alu-Repeats
Stefan Lehnert, Peter Van Loo, Pushpike J. Thilakarathne, Peter Marynen, Geert Verbeke, Frans C. Schuit
PLOS ONE , 2009, DOI: 10.1371/journal.pone.0004456
Abstract: This paper connects Alu repeats, the most abundant repetitive elements in the human genome and microRNAs, small RNAs that alter gene expression at the post-transcriptional level. Base-pair complementarity could be demonstrated between the seed sequence of a subset of human microRNAs and Alu repeats that are integrated parallel (sense) in mRNAs. The most common target site coincides with the evolutionary most conserved part of Alu. A primate-specific gene cluster on chromosome 19 encodes the majority of miRNAs that target the most conserved sense Alu site. The individual miRNA genes within this cluster are flanked by an Alu-LINE signature, which has been duplicated with the clustered miRNA genes. Gene duplication events in this locus are supported by comparing repeat length variations of the LINE elements within the cluster with those in the rest of the chromosome. Thus, a dual relationship exists between an evolutionary young miRNA cluster and their Alu targets that may have evolved in the same time window. One hypothesis for this dual relationship is that these miRNAs could protect against too high rates of duplicative transposition, which would destroy the genome.
Interdisciplinary diabetes care teams operating on the interface between primary and specialty care are associated with improved outcomes of care: findings from the Leuven Diabetes Project
Liesbeth Borgermans, Geert Goderis, Carine Van Den Broeke, Geert Verbeke, An Carbonez, Anna Ivanova, Chantal Mathieu, Bert Aertgeerts, Jan Heyrman, Richard Grol
BMC Health Services Research , 2009, DOI: 10.1186/1472-6963-9-179
Abstract: This investigation comprised a two-arm cluster randomized trial conducted in a primary care setting in Belgium. Primary care physicians (PCPs, n = 120) and their patients with type 2 diabetes mellitus (n = 2495) were included and subjects were randomly assigned to the intervention arms. The IDCT acted as a cornerstone to both the intervention arms, but the number, type and intensity of IDCT related interventions varied depending upon the intervention arm.Final registration included 67 PCPs and 1577 patients in the AQIP and 53 PCPs and 918 patients in the UQIP. 84% of the PCPs made use of the IDCT. The expected participation rate in patients (30%) was not attained, with 12,5% of the patients using the IDCT. When comparing users and non-users of the IDCT (irrespective of the intervention arm) and after 18 months of intervention the use of the IDCT was significantly associated with improvements in HbA1c, LDL-cholesterol, an increase in statins and anti-platelet therapy as well as the number of targets that were reached. When comparing users of the IDCT in the two intervention arms no significant differences were noted, except for anti-platelet therapy.IDCT's operating on the interface between primary and specialty care are associated with improved outcomes of care. More research is required on what team and program characteristics contribute to improvements in diabetes care.NTR 1369.Despite its multi-system effects, diabetes is a controllable disease, and there is unequivocal evidence that early and proactive, continuous monitoring and treatment can significantly reduce its human and economic toll [1-3]. Many guidelines provide targets that are desirable for most patients with diabetes[4]. Literature demonstrates however that many patients with type 2 diabetes mellitus (DM) still don't receive the care they need[5], as physicians overrate the quality of the care they already deliver and substantially underestimate the number of patients in need of intensified pharmacot
Effectiveness of the introduction of a Chronic Care Model-based program for type 2 diabetes in Belgium
Patricia Sunaert, Hilde Bastiaens, Frank Nobels, Luc Feyen, Geert Verbeke, Etienne Vermeire, Jan De Maeseneer, Sara Willems, An De Sutter
BMC Health Services Research , 2010, DOI: 10.1186/1472-6963-10-207
Abstract: A quasi-experimental study design involving a control region with comparable geographical and socio-economic characteristics and health care facilities was used to evaluate the effect of the intervention in the region. In collaboration with the InterMutualistic Agency (IMA) and the laboratories from both regions a research database was set up. Study cohorts in both regions were defined by using administrative data from the Sickness Funds and selected from the research database. A set of nine quality indicators was defined based on current scientific evidence. Data were analysed by an institution experienced in longitudinal data analysis.In total 4,174 type 2 diabetes patients were selected from the research database; 2,425 patients (52.9% women) with a mean age of 67.5 from the intervention region and 1,749 patients (55.7% women) with a mean age of 67.4 from the control region. At the end of the intervention period, improvements were observed in five of the nine defined quality indicators in the intervention region, three of which (HbA1c assessment, statin therapy, cholesterol target) improved significantly more than in the control region. Mean HbA1c improved significantly in the intervention region (7.55 to 7.06%), but this evolution did not differ significantly (p = 0.4207) from the one in the control region (7.44 to 6.90%). The improvement in lipid control was significantly higher (p = 0.0021) in the intervention region (total cholesterol 199.07 to 173 mg/dl) than in the control region (199.44 to 180.60 mg/dl). The systematic assessment of long-term diabetes complications remained insufficient. In 2006 only 26% of the patients had their urine tested for micro-albuminuria and only 36% had consulted an ophthalmologist.Although the overall ACIC score increased from 1.45 to 5.5, the improvement in the quality of diabetes care was moderate. Further improvements are needed in the CCM components delivery system design and clinical information systems. The regional netwo
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