scholarly journals The dyspnoea–inactivity vicious circle in COPD: development and external validation of a conceptual model

2018 ◽  
Vol 52 (3) ◽  
pp. 1800079 ◽  
Author(s):  
Maria A. Ramon ◽  
Gerben Ter Riet ◽  
Anne-Elie Carsin ◽  
Elena Gimeno-Santos ◽  
Alvar Agustí ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Exercise Capacity ◽  
Structural Equation ◽  
Structural Equation Models ◽  
External Validation ◽  
Model Fit ◽  
Vicious Circle ◽  
Chronic Obstructive ◽  
New Model ◽  
Copd Exacerbations

The vicious circle of dyspnoea–inactivity has been proposed, but never validated empirically, to explain the clinical course of chronic obstructive pulmonary disease (COPD). We aimed to develop and validate externally a comprehensive vicious circle model.We utilised two methods. 1) Identification and validation of all published vicious circle models by a systematic literature search and fitting structural equation models to longitudinal data from the Spanish PAC-COPD (Phenotype and Course of COPD) cohort (n=210, mean age 68 years, mean forced expiratory volume in 1 s (FEV1) 54% predicted), testing both the hypothesised relationships between variables in the model (“paths”) and model fit. 2) Development of a new model and external validation using longitudinal data from the Swiss and Dutch ICE COLD ERIC (International Collaborative Effort on Chronic Obstructive Lung Disease: Exacerbation Risk Index Cohorts) cohort (n=226, mean age 66 years, mean FEV157% predicted).We identified nine vicious circle models for which structural equation models confirmed most hypothesised paths but showed inappropriate fit. In the new model, airflow limitation, hyperinflation, dyspnoea, physical activity, exercise capacity and COPD exacerbations remained related to other variables and model fit was appropriate. Fitting it to ICE COLD ERIC, all paths were replicated and model fit was appropriate.Previously published vicious circle models do not fully explain the vicious circle concept. We developed and externally validated a new comprehensive model that gives a more relevant role to exercise capacity and COPD exacerbations.

Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽  
2014 ◽  
Vol 10 (4) ◽  
pp. 138-152 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Susan Troncoso Skidmore ◽  
Yan Li ◽  
Bruce Thompson
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Equation Model ◽  
Model Fit ◽  
Model Specification ◽  
Simulation Design ◽  
Measurement Models ◽  
Fit Statistics ◽  
Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

scholarly journals Structural equation modelling the relationship between anti-fungal prophylaxis and Pseudomonas bacteremia in ICU patients.

2021 ◽  
Author(s):  
James Hurley
Keyword(s):  
Latent Variables ◽  
Structural Equation ◽  
Bacterial Infections ◽  
Structural Equation Models ◽  
Model Fit ◽  
Published Data ◽  
Icu Patients ◽  
Equation Modelling ◽  
Anti Fungal ◽  
The Relationship

Abstract Purpose Animal models implicate candida colonization facilitating invasive bacterial infections. The clinical relevance of this microbial interaction remains undefined. Observations from studies of anti-septic, antibiotic, anti-fungal, and non-decontamination-based interventions to prevent ICU acquired infection collectively serve as a natural experiment. Methods Three candidate generalized structural equation models (GSEM), with Candida and Pseudomonas colonization as latent variables, were confronted with blood culture and respiratory tract isolate data derived from 460 groups from 279 studies including studies of combined antibiotic and antifungal exposures within selective digestive decontamination (SDD) interventions. Results Introducing an interaction term between Candida colonization and Pseudomonas colonization substantially improved GSEM model fit. Model derived coefficients for singular exposure to anti-septic agents (-1.23; -2.1 to -0.32), amphotericin (-1.78; -2.79 to -0.78) and topical antibiotic prophylaxis (TAP; +1.02; +0.11 to + 1.93) versus Candida colonization were similar in magnitude but contrary in direction. By contrast, the model-derived coefficients for singular exposure to TAP, as with anti-septic agents, versus Pseudomonas colonization were weaker or non-significant. Singular exposure to amphotericin would be predicted to more than halve candidemia and Pseudomonas bacteremia incidences versus literature benchmarks for absolute differences of approximately one percentage point or less. Conclusion GSEM modelling of published data supports the postulated interaction between Candida and Pseudomonas colonization towards promoting bacteremia among ICU patients. The model implicates that anti-fungal agents have greater impact in preventing bacteremia versus TAP, which has no impact.

Exploring shy minds: Relations between shyness and creativity

2018 ◽  
Author(s):  
Maria Magdalena Kwiatkowska ◽  
Radosław Rogoza ◽  
Kristie L. Poole
Keyword(s):  
Young Adults ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Model Fit ◽  
Intellectual Curiosity ◽  
Creative Imagination ◽  
Personality Dimensions ◽  
Negatively Associated

The relation between shyness and creativity has not been fully elucidated. The aim of present study was to examine if shy individuals express their creativity in different ways, as indexed by the relation between shyness and open-mindedness and its three facets: intellectual curiosity, aesthetic sensitivity, and creative imagination. We analyzed two separate structural equation models: first treating overall open-mindedness as a predictor of shyness, and the second in which we examined the three separate facets of open-mindedness (i.e., intellectual curiosity, aesthetic sensitivity, and creative imagination). Young adults (N = 727, Mage = 22.19 years) self-reported their levels of shyness and the three facets of open-mindedness. Results revealed that although shyness was unrelated to the broader construct of open-mindedness, the differentiation of the open-mindedness facets resulted in a good model fit to the data. Specifically, shyness was negatively associated with creative imagination, but positively associated with aesthetic sensitivity. Our findings illustrate the importance of examining different components of creativity, as these seem to be differentially associated with personality dimensions such as shyness. Our findings also have theoretical implications for understanding how shy individuals may express their creativity.

scholarly journals The Use of Mixed Models for the Analysis of Mediated Data with Time-Dependent Predictors

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Emily A. Blood ◽  
Debbie M. Cheng
Keyword(s):  
Longitudinal Data ◽  
Mixed Models ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Type I Error ◽  
Time Dependent ◽  
Type I ◽  
Delayed Effects ◽  
Causal Pathways ◽  
Observational Cohort

Linear mixed models (LMMs) are frequently used to analyze longitudinal data. Although these models can be used to evaluate mediation, they do not directly model causal pathways. Structural equation models (SEMs) are an alternative technique that allows explicit modeling of mediation. The goal of this paper is to evaluate the performance of LMMs relative to SEMs in the analysis of mediated longitudinal data with time-dependent predictors and mediators. We simulated mediated longitudinal data from an SEM and specified delayed effects of the predictor. A variety of model specifications were assessed, and the LMMs and SEMs were evaluated with respect to bias, coverage probability, power, and Type I error. Models evaluated in the simulation were also applied to data from an observational cohort of HIV-infected individuals. We found that when carefully constructed, the LMM adequately models mediated exposure effects that change over time in the presence of mediation, even when the data arise from an SEM.

scholarly journals Causality on longitudinal data: Stable specification search in constrained structural equation modeling

2017 ◽  
Vol 27 (12) ◽  
pp. 3814-3834 ◽  
Author(s):  
Ridho Rahmadi ◽  
Perry Groot ◽  
Marieke HC van Rijn ◽  
Jan AJG van den Brand ◽  
Marianne Heins ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Chronic Fatigue ◽  
Causal Modeling ◽  
Model Complexity ◽  
Equation Modeling ◽  
Data Set ◽  
Finite Data

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.

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