Comorbidity: A network perspective

2010 ◽  
Vol 33 (2-3) ◽  
pp. 137-150 ◽  
Author(s):  
Angélique O. J. Cramer ◽  
Lourens J. Waldorp ◽  
Han L. J. van der Maas ◽  
Denny Borsboom
Keyword(s):  
Major Depression ◽  
Mental Disorder ◽  
Latent Variables ◽  
Latent Variable ◽  
Latent Variable Models ◽  
The Other ◽  
Generalized Anxiety ◽  
Network Approach ◽  
Variable Theory ◽  
Diagnostic Categories

AbstractThe pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy.

Latent variable models are network models

2010 ◽  
Vol 33 (2-3) ◽  
pp. 166-166 ◽  
Author(s):  
Peter C. M. Molenaar
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Network Models ◽  
Latent Variable Models ◽  
Variable Theory ◽  
The Relationship

AbstractCramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.

Symptoms as latent variables

2010 ◽  
Vol 33 (2-3) ◽  
pp. 165-166 ◽  
Author(s):  
Dennis J. McFarland ◽  
Loretta S. Malta
Keyword(s):  
Anxiety Disorder ◽  
Generalized Anxiety Disorder ◽  
Latent Variables ◽  
Diagnosis And Treatment ◽  
Diagnostic Classification ◽  
Generalized Anxiety ◽  
Diagnostic Categories ◽  
Target Article ◽  
The Relationship

AbstractIn the target article, Cramer et al. suggest that diagnostic classification is improved by modeling the relationship between manifest variables (i.e., symptoms) rather than modeling unobservable latent variables (i.e., diagnostic categories such as Generalized Anxiety Disorder). This commentary discusses whether symptoms represent manifest or latent variables and the implications of this distinction for diagnosis and treatment.

scholarly journals Supervised Strategies of Multiblock Data Analysis: A Unified Approach and Extension

2021 ◽  
Author(s):  
Essomanda Tchandao Mangamana ◽  
Romain Lucas Glele Kakai ◽  
El Mostafa Qannari
Keyword(s):  
Data Analysis ◽  
Latent Variables ◽  
Latent Variable ◽  
Redundancy Analysis ◽  
The Other ◽  
Pls Regression ◽  
Unified Approach ◽  
Optimization Criteria ◽  
Supervised Methods

Within the framework of multiblock data analysis, a unified approach of supervised methods is discussed. It encompasses multiblock redundancy analysis (MB-RA) and multiblock partial least squares (MB-PLS) regression. Moreover, we develop new supervised strategies of multiblock data analysis, which can be seen as variants of one or the other of these two methods. They are respectively refered to as multiblock weighted redundancy analysis (MB-WRA) and multiblock weighted covariate analysis (MB-WCov). The four methods are based on the determination of latent variables associated with the various blocks of variables. They are derived from clear optimization criteria whose aim is to maximize either the sum of the covariances or the sum of squared covariances between the latent variable associated with the response block of variables and the block latent variables associated with the various explanatory blocks of variables. We also propose indices to help better interpreting the outcomes of the analyses. The methods are illustrated and compared based on simulated and real datasets.

scholarly journals Causal Effects Based on Latent Variable Models

2019 ◽  
Author(s):  
Axel Mayer
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Stochastic Theory ◽  
Latent Variable Models ◽  
Causal Effects ◽  
Equation Modeling ◽  
Differential Effectiveness ◽  
Measurement Models ◽  
Effect Model

Building on the stochastic theory of causal effects and latent state-trait theory, this article shows how a comprehensive analysis of the effectiveness of interventions can be conducted based on latent variable models. The proposed approach offers new ways to evaluate the differential effectiveness of interventions for substantive researchers in experimental and observational studies while allowing for complex measurement models. The key definitions and assumptions of the stochastic theory of causal effects are first introduced and then four statistical models that can be used to estimate various types of causal effects with latent state-trait models are developed and illustrated: The multistate effect model with and without method factors, the true-change effect model, and the multitrait effect model. All effect models with latent variables are implemented based on multigroup structural equation modeling with the EffectLiteR approach. Particular emphasis is placed on the development of models with interactions that allow for interindividual differences in treatment effects based on latent variables. Open source software code is provided for all models.

Some mental disorders are based on networks, others on latent variables

2010 ◽  
Vol 33 (2-3) ◽  
pp. 166-167 ◽  
Author(s):  
Don Ross
Keyword(s):  
Major Depressive Disorder ◽  
Anxiety Disorder ◽  
Mental Disorders ◽  
Depressive Disorder ◽  
Latent Variables ◽  
Latent Variable ◽  
Generalized Anxiety ◽  
Major Depressive ◽  
Axis I Disorders ◽  
Axis I

AbstractCramer et al. persuasively conceptualize major depressive disorder (MDD) and generalized anxiety disorder (GAD) as network disorders, rejecting latent variable accounts. But how does their radical picture generalize across the suite of mental and personality disorders? Addictions are Axis I disorders that may be better characterized by latent variables. Their comorbidity relationships could be captured by inserting them as nodes in a super-network of Axis I conditions.

scholarly journals Causal Effects Based on Latent Variable Models

Methodology ◽  
2019 ◽  
Vol 15 (Supplement 1) ◽  
pp. 15-28 ◽  
Author(s):  
Axel Mayer
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Stochastic Theory ◽  
Latent Variable Models ◽  
Causal Effects ◽  
Equation Modeling ◽  
Measurement Models ◽  
Effect Model ◽  
Multigroup Structural Equation Modeling

Abstract. Building on the stochastic theory of causal effects and latent state-trait theory, this article shows how a comprehensive analysis of the effects of interventions can be conducted based on latent variable models. The proposed approach offers new ways to evaluate the differential effects of interventions for substantive researchers in experimental and observational studies while allowing for complex measurement models. The key definitions and assumptions of the stochastic theory of causal effects are first introduced and then four statistical models that can be used to estimate various types of causal effects with latent state-trait models are developed and illustrated: The multistate effect model with and without method factors, the true-change effect model, and the multitrait effect model. All effect models with latent variables are implemented based on multigroup structural equation modeling with the EffectLiteR approach. Particular emphasis is placed on the development of models with interactions that allow for interindividual differences in treatment effects based on latent variables. Open source software code is provided for all models.

scholarly journals Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models

2018 ◽  
Author(s):  
Matthew R Whiteway ◽  
Karolina Socha ◽  
Vincent Bonin ◽  
Daniel A Butts
Keyword(s):  
Neural Activity ◽  
Latent Variables ◽  
Latent Variable ◽  
Large Scale ◽  
Latent Variable Models ◽  
Neural Population ◽  
Variable Model ◽  
Experimental Conditions ◽  
Affine Model ◽  
Nonlinear Latent Variable Models

AbstractSensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the information contained in those responses. Such variability is often shared across many neurons, which in principle can allow a decoder to mitigate the effects of such noise, depending on the structure of the shared variability and its relationship to sensory encoding at the population level. Latent variable models offer an approach for characterizing the structure of this shared variability in neural population recordings, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we leverage recent advances in machine learning to introduce two nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neuron’s stimulus selectivity and a set of latent variables that modulate these stimulus responses both additively and multiplicatively. While these approaches can detect general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an “affine model”. In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions.

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