The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
Catherine Matias - One of the best experts on this subject based on the ideXlab platform.
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Inferring sparse Gaussian graphical models with Latent Structure
Electronic Journal of Statistics, 2009Co-Authors: Christophe Ambroise, Julien Chiquet, Catherine MatiasAbstract:Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional dependencies between the variables. We describe a novel framework taking into account a Latent Structure on the concentration matrix. This Latent Structure is used to drive a penalty matrix and thus to recover a graphical model with a constrained topology. Our method uses an $\ell_1$ penalized likelihood criterion. Inference of the graph of conditional dependencies between the variates and of the hidden variables is performed simultaneously in an iterative \textsc{em}-like algorithm. The performances of our method is illustrated on synthetic as well as real data, the latter concerning breast cancer.
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Inferring sparse Gaussian graphical models with Latent Structure
Electronic journal of statistics, 2009Co-Authors: Christophe Ambroise, Julien Chiquet, Catherine MatiasAbstract:Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional dependencies between the variables. We describe a novel framework taking into account a Latent Structure on the concentration matrix. This Latent Structure is used to drive a penalty matrix and thus to recover a graphical model with a constrained topology. Our method uses an $\ell_1$ penalized likelihood criterion. Inference of the graph of conditional dependencies between the variates and of the hidden variables is performed simultaneously in an iterative EM-like algorithm named SIMoNe (Statistical Inference for Modular Networks). Performances are illustrated on synthetic as well as real data, the latter concerning breast cancer. For gene regulation networks, our method can provide a useful insight both on the mutual influence existing between genes, and on the modules existing in the network.
Fabian Jasper - One of the best experts on this subject based on the ideXlab platform.
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The Latent Structure of the functional dyspepsia symptom complex: a taxometric analysis.
Neurogastroenterology and motility : the official journal of the European Gastrointestinal Motility Society, 2016Co-Authors: L. Van Oudenhove, Fabian Jasper, Michael Witthoft, Marta Walentynowicz, O. Van Den Bergh, Jan TackAbstract:OBJECTIVES Rome III introduced a subdivision of functional dyspepsia (FD) into postprandial distress syndrome and epigastric pain syndrome, characterized by early satiation/postprandial fullness, and epigastric pain/burning, respectively. However, evidence on their degree of overlap is mixed. We aimed to investigate the Latent Structure of FD to test whether distinguishable symptom-based subgroups exist. METHODS Consecutive tertiary care Rome II FD patients completed the dyspepsia symptom severity scale. Confirmatory factor analysis (CFA) was used to compare the fit of a single factor model, a correlated three-factor model based on Rome III subgroups and a bifactor model consisting of a general FD factor and orthogonal subgroup factors. Taxometric analyses were subsequently used to investigate the Latent Structure of FD. KEY RESULTS Nine hundred and fifty-seven FD patients (71.1% women, age 41 ± 14.8) participated. In CFA, the bifactor model yielded a significantly better fit than the two other models (χ² difference tests both p 0.05). Taxometric analyses supported a dimensional Structure of FD (all CCFI
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The Latent Structure of Medically Unexplained Symptoms and Its Relation to Functional Somatic Syndromes
International Journal of Behavioral Medicine, 2013Co-Authors: Michael Witthoft, Wolfgang Hiller, Noelle Loch, Fabian JasperAbstract:Background Medically unexplained symptoms are the hallmark of somatoform disorders and functional somatic syndromes. Purpose Although medically unexplained symptoms represent a common phenomenon both in the general population as well as in medical settings, the exact Latent Structure of somatic symptoms remains largely unclear. Method We examined the Latent Structure of medically unexplained symptoms by means of the Patient Health Questionnaire-15 (PHQ-15) questionnaire (i.e., a popular symptom checklist) and provide support for the construct validity of our model. The data were analyzed using confirmatory factor analysis in a general population sample (study 1; N = 414) and in a sample of primary care patients (study 2; N = 308). We compared four different Latent Structure models of medically unexplained symptoms: a general factor model, a correlated group factor model, a hierarchical model, and a bifactor model. Results In study 1, a bifactor model with one general factor and four independent specific symptom factors (i.e., gastrointestinal, pain, fatigue, and cardiopulmonary symptoms) showed the best model fit. This bifactor model was confirmed in the primary care sample (study 2). Additionally, the model explained 59 % of the variance of the irritable bowel syndrome (IBS). In this structural equation model, both the general factor (14 %) as well as the gastrointestinal symptom factor (42 %) significantly predicted the IBS. Conclusion The findings of both studies help to clarify the Latent Structure of somatic symptoms in the PHQ-15. The bifactor model outperformed alternative models and demonstrated external validity in predicting IBS.
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somatic symptom reporting has a dimensional Latent Structure results from taxometric analyses
Journal of Abnormal Psychology, 2012Co-Authors: Fabian Jasper, Josef Bailer, Fred Rist, Wolfgang Hiller, Michael WitthoftAbstract:Medically unexplained symptoms (MUS) are one of the key features of somatoform disorders. Although MUS are currently treated as both categorical (in terms of the diagnosis of somatoform disorders) and dimensional (in terms of quantitative measures of somatization/somatic symptom reporting), little is known about the empirical Latent Structure of MUS. Using taxometric analyses, the Latent Structure of somatic symptom reporting was analyzed with the Patient Health Questionnaire (PHQ)-15 in two student samples (N=782 and N=2,577) and a primary care sample (N=519). We applied three popular taxometric methods: Maximum Eigenvalue (MAXEIG), Mean Above Minus Below a Cut (MAMBAC) and Latent-Mode (L-Mode). Simulation data were created to evaluate the appropriateness of the data for our analyses and to create the comparison curve fit index (CCFI) as an objective outcome measure. The results of all taxometric methods in any of the three data sets were in favor of a dimensional solution (CCFI<.50). Simulated taxonic and dimensional datasets differed substantially and the samples were appropriate for taxometric analysis. Accordingly, the Latent Structure of somatization/somatic symptom reporting as assessed by the PHQ-15 is dimensional in both primary care and student samples. Implications regarding the practical application as well as models of etiology and pathogenesis of somatic symptom reporting are discussed.
Michael Witthoft - One of the best experts on this subject based on the ideXlab platform.
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The Latent Structure of the functional dyspepsia symptom complex: a taxometric analysis.
Neurogastroenterology and motility : the official journal of the European Gastrointestinal Motility Society, 2016Co-Authors: L. Van Oudenhove, Fabian Jasper, Michael Witthoft, Marta Walentynowicz, O. Van Den Bergh, Jan TackAbstract:OBJECTIVES Rome III introduced a subdivision of functional dyspepsia (FD) into postprandial distress syndrome and epigastric pain syndrome, characterized by early satiation/postprandial fullness, and epigastric pain/burning, respectively. However, evidence on their degree of overlap is mixed. We aimed to investigate the Latent Structure of FD to test whether distinguishable symptom-based subgroups exist. METHODS Consecutive tertiary care Rome II FD patients completed the dyspepsia symptom severity scale. Confirmatory factor analysis (CFA) was used to compare the fit of a single factor model, a correlated three-factor model based on Rome III subgroups and a bifactor model consisting of a general FD factor and orthogonal subgroup factors. Taxometric analyses were subsequently used to investigate the Latent Structure of FD. KEY RESULTS Nine hundred and fifty-seven FD patients (71.1% women, age 41 ± 14.8) participated. In CFA, the bifactor model yielded a significantly better fit than the two other models (χ² difference tests both p 0.05). Taxometric analyses supported a dimensional Structure of FD (all CCFI
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The Latent Structure of Medically Unexplained Symptoms and Its Relation to Functional Somatic Syndromes
International Journal of Behavioral Medicine, 2013Co-Authors: Michael Witthoft, Wolfgang Hiller, Noelle Loch, Fabian JasperAbstract:Background Medically unexplained symptoms are the hallmark of somatoform disorders and functional somatic syndromes. Purpose Although medically unexplained symptoms represent a common phenomenon both in the general population as well as in medical settings, the exact Latent Structure of somatic symptoms remains largely unclear. Method We examined the Latent Structure of medically unexplained symptoms by means of the Patient Health Questionnaire-15 (PHQ-15) questionnaire (i.e., a popular symptom checklist) and provide support for the construct validity of our model. The data were analyzed using confirmatory factor analysis in a general population sample (study 1; N = 414) and in a sample of primary care patients (study 2; N = 308). We compared four different Latent Structure models of medically unexplained symptoms: a general factor model, a correlated group factor model, a hierarchical model, and a bifactor model. Results In study 1, a bifactor model with one general factor and four independent specific symptom factors (i.e., gastrointestinal, pain, fatigue, and cardiopulmonary symptoms) showed the best model fit. This bifactor model was confirmed in the primary care sample (study 2). Additionally, the model explained 59 % of the variance of the irritable bowel syndrome (IBS). In this structural equation model, both the general factor (14 %) as well as the gastrointestinal symptom factor (42 %) significantly predicted the IBS. Conclusion The findings of both studies help to clarify the Latent Structure of somatic symptoms in the PHQ-15. The bifactor model outperformed alternative models and demonstrated external validity in predicting IBS.
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somatic symptom reporting has a dimensional Latent Structure results from taxometric analyses
Journal of Abnormal Psychology, 2012Co-Authors: Fabian Jasper, Josef Bailer, Fred Rist, Wolfgang Hiller, Michael WitthoftAbstract:Medically unexplained symptoms (MUS) are one of the key features of somatoform disorders. Although MUS are currently treated as both categorical (in terms of the diagnosis of somatoform disorders) and dimensional (in terms of quantitative measures of somatization/somatic symptom reporting), little is known about the empirical Latent Structure of MUS. Using taxometric analyses, the Latent Structure of somatic symptom reporting was analyzed with the Patient Health Questionnaire (PHQ)-15 in two student samples (N=782 and N=2,577) and a primary care sample (N=519). We applied three popular taxometric methods: Maximum Eigenvalue (MAXEIG), Mean Above Minus Below a Cut (MAMBAC) and Latent-Mode (L-Mode). Simulation data were created to evaluate the appropriateness of the data for our analyses and to create the comparison curve fit index (CCFI) as an objective outcome measure. The results of all taxometric methods in any of the three data sets were in favor of a dimensional solution (CCFI<.50). Simulated taxonic and dimensional datasets differed substantially and the samples were appropriate for taxometric analysis. Accordingly, the Latent Structure of somatization/somatic symptom reporting as assessed by the PHQ-15 is dimensional in both primary care and student samples. Implications regarding the practical application as well as models of etiology and pathogenesis of somatic symptom reporting are discussed.
Gregory P. Strauss - One of the best experts on this subject based on the ideXlab platform.
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network analysis reveals the Latent Structure of negative symptoms in schizophrenia
Schizophrenia Bulletin, 2019Co-Authors: Gregory P. Strauss, Brian Kirkpatrick, Farnaz Zamani Esfahlani, Silvana Galderisi, A Mucci, Alessandro Rossi, Paola Bucci, Paola Rocca, Mario Maj, Ivan RuizAbstract:Prior studies using exploratory factor analysis provide evidence that negative symptoms are best conceptualized as 2 dimensions reflecting diminished motivation and expression. However, the 2-dimensional model has yet to be evaluated using more complex mathematical techniques capable of testing Structure. In the current study, network analysis was applied to evaluate the Latent Structure of negative symptoms using a community-detection algorithm. Two studies were conducted that included outpatients with schizophrenia (SZ; Study 1: n = 201; Study 2: n = 912) who were rated on the Brief Negative Symptom Scale (BNSS). In both studies, network analysis indicated that the 13 BNSS items divided into 6 negative symptom domains consisting of anhedonia, avolition, asociality, blunted affect, alogia, and lack of normal distress. Separation of these domains was statistically significant with reference to a null model of randomized networks. There has been a recent trend toward conceptualizing the Latent Structure of negative symptoms in relation to 2 distinct dimensions reflecting diminished expression and motivation. However, the current results obtained using network analysis suggest that the 2-dimensional conceptualization is not complex enough to capture the nature of the negative symptom construct. Similar to recent confirmatory factor analysis studies, network analysis revealed that the Latent Structure of negative symptom is best conceptualized in relation to the 5 domains identified in the 2005 National Institute of Mental Health consensus development conference (anhedonia, avolition, asociality, blunted affect, and alogia) and potentially a sixth domain consisting of lack of normal distress. Findings have implications for identifying pathophysiological mechanisms and targeted treatments.
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The Latent Structure of Negative Symptoms in Schizophrenia.
JAMA psychiatry, 2018Co-Authors: Gregory P. Strauss, Alicia Nuñez, Anthony O. Ahmed, Kimberly A. Barchard, Eric Granholm, Brian Kirkpatrick, James M. Gold, Daniel N. AllenAbstract:Importance Negative symptoms are associated with a range of poor clinical outcomes, and currently available treatments generally do not produce a clinically meaningful response. Limited treatment progress may be owing in part to poor clarity regarding Latent Structure. Prior studies have inferred Latent Structure using exploratory factor analysis, which has led to the conclusion that there are 2 dimensions reflecting motivation and pleasure (MAP) and diminished expressivity (EXP) factors. However, whether these conclusions are statistically justified remains unclear because exploratory factor analysis does not test Latent Structure. Confirmatory factor analysis (CFA) is needed to test competing models regarding the Latent Structure of a construct. Objective To evaluate the fit of 4 models of the Latent Structure of negative symptoms in schizophrenia using CFA. Design, Setting, and Participants Three cross-sectional studies were conducted on outpatients with schizophrenia who were rated on the 3 most conceptually contemporary measures: Scale for the Assessment of Negative Symptoms (SANS), Brief Negative Symptom Scale (BNSS), and Clinical Assessment Interview for Negative Symptoms (CAINS). Confirmatory factor analysis evaluated the following 4 models: (1) a 1-factor model; (2) a 2-factor model with EXP and MAP factors; (3) a 5-factor model with separate factors for the 5 domains of the National Institute of Mental Health consensus development conference (blunted affect, alogia, anhedonia, avolition, and asociality); and (4) a hierarchical model with 2 second-order factors reflecting EXP and MAP and 5 first-order factors reflecting the 5 consensus domains. Main Outcomes and Measures Outcomes included CFA model fit statistics derived from symptom severity scores on the SANS, BNSS, and CAINS. Results The study population included 860 outpatients with schizophrenia (68.0% male; mean [SD] age, 43.0 [11.4] years). Confirmatory factor analysis was conducted on each scale, including 268 patients for the SANS, 192 for the BNSS, and 400 for the CAINS. The 1- and 2-factor models provided poor fit for the SANS, BNSS, and CAINS as indicated by comparative fit indexes (CFIs) and Tucker Lewis indexes (TLIs) less than 0.950, RMSEAs that exceeded the 0.080 threshold, and WRMRs greater than 1.00. The 5-factor and hierarchical models provided excellent fit, with the 5-factor model being more parsimonious. The CFIs and TLIs met the 0.95 threshold and the 1.00 threshold for both factor models with all 3 measures. Interestingly, the RMSEAs for the 5-factor model and the hierarchical model fell under the 0.08 threshold for the BNSS and the CAINS but not the SANS. Conclusions and Relevance These findings suggest that the recent trend toward conceptualizing the Latent Structure of negative symptoms as 2 distinct dimensions does not adequately capture the complexity of the construct. The Latent Structure of negative symptoms is best conceptualized in relation to the 5 consensus domains. Implications for identifying pathophysiological mechanisms and targeted treatments are discussed.
Christophe Ambroise - One of the best experts on this subject based on the ideXlab platform.
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Inferring sparse Gaussian graphical models with Latent Structure
Electronic Journal of Statistics, 2009Co-Authors: Christophe Ambroise, Julien Chiquet, Catherine MatiasAbstract:Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional dependencies between the variables. We describe a novel framework taking into account a Latent Structure on the concentration matrix. This Latent Structure is used to drive a penalty matrix and thus to recover a graphical model with a constrained topology. Our method uses an $\ell_1$ penalized likelihood criterion. Inference of the graph of conditional dependencies between the variates and of the hidden variables is performed simultaneously in an iterative \textsc{em}-like algorithm. The performances of our method is illustrated on synthetic as well as real data, the latter concerning breast cancer.
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Inferring sparse Gaussian graphical models with Latent Structure
Electronic journal of statistics, 2009Co-Authors: Christophe Ambroise, Julien Chiquet, Catherine MatiasAbstract:Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional dependencies between the variables. We describe a novel framework taking into account a Latent Structure on the concentration matrix. This Latent Structure is used to drive a penalty matrix and thus to recover a graphical model with a constrained topology. Our method uses an $\ell_1$ penalized likelihood criterion. Inference of the graph of conditional dependencies between the variates and of the hidden variables is performed simultaneously in an iterative EM-like algorithm named SIMoNe (Statistical Inference for Modular Networks). Performances are illustrated on synthetic as well as real data, the latter concerning breast cancer. For gene regulation networks, our method can provide a useful insight both on the mutual influence existing between genes, and on the modules existing in the network.
