scholarly journals Reliability of a lifetime history of major depression: implications for heritability and co-morbidity

1998 ◽  
Vol 28 (4) ◽  
pp. 857-870 ◽  
Author(s):  
D. L. FOLEY ◽  
M. C. NEALE ◽  
K. S. KENDLER
Keyword(s):  
Major Depression ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Familial Risk ◽  
Ratio Test ◽  
Psychiatric Interview ◽  
Lifetime History ◽  
Co Morbidity ◽  
Familial Influences ◽  
History Of

Background. In unselected samples, the diagnosis of major depression (MD) is not highly reliable. It is not known if occasion-specific influences on reliability index familial risk factors for MD, or how reliability is associated with risk for co-morbid anxiety disorders.Methods. An unselected sample of 847 female twin pairs was interviewed twice, 5 years apart, about their lifetime history (LTH) of MD, generalized anxiety disorder (GAD) and panic disorder (PD). Familial influences on reliability were examined using structural equation models. Logistic regression was used to identify clinical features that predict reliable diagnosis. Co-morbidity was characterized using the continuation ratio test.Results. The reliability of a LTH of MD over 5 years was fair (κ=0·43). There was no evidence for occasion-specific familial influences on reliability, and heritability of reliably diagnosed MD was estimated at 66%. Subjects with unreliably diagnosed MD reported fewer symptoms and, if diagnosed with MD only at the first interview, less impairment and help seeking, or, if diagnosed with MD only at the second interview, fewer episodes and a longer illness. A history of co-morbid GAD or PD is more prevalent among subjects with reliably diagnosed MD.Conclusions. A diagnosis of MD based on a single psychiatric interview incorporates a substantial amount of measurement error but there is no evidence that transient influences on recall and diagnosis index familial risk for MD. Quantitative indices of risk for MD based on multiple interviews should reflect both the characteristics of MD and the temporal order of positive diagnoses.

scholarly journals Increased Hippocampal Plaques and Tangles in Patients With Alzheimer Disease With a Lifetime History of Major Depression

2006 ◽  
Vol 63 (2) ◽  
pp. 161 ◽  
Author(s):  
Michael A. Rapp ◽  
Michal Schnaider-Beeri ◽  
Hillel T. Grossman ◽  
Mary Sano ◽  
Daniel P. Perl ◽  
...  
Keyword(s):  
Major Depression ◽  
Alzheimer Disease ◽  
Lifetime History ◽  
History Of

scholarly journals Analytical power calculations for structural equation modeling: A tutorial and Shiny app

2020 ◽  
Author(s):  
Suzanne Jak ◽  
Terrence D. Jorgensen ◽  
Mathilde G. E. Verdam ◽  
Frans J. Oort ◽  
Louise Elffers
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Statistical Significance ◽  
Equation Modeling ◽  
Ratio Test ◽  
Power Calculations ◽  
Shiny App ◽  
Computationally Intensive

Abstract Conducting a power analysis can be challenging for researchers who plan to analyze their data using structural equation models (SEMs), particularly when Monte Carlo methods are used to obtain power. In this tutorial, we explain how power calculations without Monte Carlo methods for the χ2 test and the RMSEA tests of (not-)close fit can be conducted using the Shiny app “power4SEM”. power4SEM facilitates power calculations for SEM using two methods that are not computationally intensive and that focus on model fit instead of the statistical significance of (functions of) parameters. These are the method proposed by Satorra and Saris (Psychometrika 50(1), 83–90, 1985) for power calculations of the likelihood ratio test, and that described by MacCallum, Browne, and Sugawara (Psychol Methods 1(2) 130–149, 1996) for RMSEA-based power calculations. We illustrate the use of power4SEM with examples of power analyses for path models, factor models, and a latent growth model.

S41GENOMIC STRUCTURAL EQUATION MODELS OF MAJOR DEPRESSION SYMPTOMS

2019 ◽  
Vol 29 ◽  
pp. S135
Author(s):  
Mark Adams ◽  
Andrew Grotzinger ◽  
Bradley Jermy ◽  
Jackson Thorp ◽  
Michel Nivard ◽  
...  
Keyword(s):  
Major Depression ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Depression Symptoms

scholarly journals A 'Weight of Evidence' approach to evaluating structural equation models

One Ecosystem ◽  
2020 ◽  
Vol 5 ◽  
Author(s):  
James Grace
Keyword(s):  
Structural Equation Modeling ◽  
Model Selection ◽  
Model Evaluation ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Weight Of Evidence ◽  
Equation Modeling ◽  
Future Studies ◽  
Current State ◽  
History Of

It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complex and difficult to integrate with current approaches used in biology. Opposition to the use of p-values and decision thresholds has been voiced by the statistics community, yet certain phases of model evaluation have been historically tied to reliance on p-values. In this paper, I outline an approach to model evaluation, comparison and selection based on a weight-of-evidence paradigm. The details and proposed sequence of steps are illustrated using a real-world example. At the end of the paper, I briefly discuss the current state of knowledge and a possible direction for future studies.

scholarly journals Familial risk for distress and fear disorders and emotional reactivity in adolescence: an event-related potential investigation

2015 ◽  
Vol 45 (12) ◽  
pp. 2545-2556 ◽  
Author(s):  
B. D. Nelson ◽  
G. Perlman ◽  
G. Hajcak ◽  
D. N. Klein ◽  
R. Kotov
Keyword(s):  
Adolescent Girls ◽  
Emotional Reactivity ◽  
Familial Risk ◽  
Event Related Potential ◽  
Parental Distress ◽  
Emotional Stimuli ◽  
Parental History ◽  
Lifetime History ◽  
History Of ◽  
History Of Depression

BackgroundThe late positive potential (LPP) is an event-related potential component that is sensitive to the motivational salience of stimuli. Children with a parental history of depression, an indicator of risk, have been found to exhibit an attenuated LPP to emotional stimuli. Research on depressive and anxiety disorders has organized these conditions into two empirical classes: distress and fear disorders. The present study examined whether parental history of distress and fear disorders was associated with the LPP to emotional stimuli in a large sample of adolescent girls.MethodThe sample of 550 girls (ages 13.5–15.5 years) with no lifetime history of depression completed an emotional picture-viewing task and the LPP was measured in response to neutral, pleasant and unpleasant pictures. Parental lifetime history of psychopathology was determined via a semi-structured diagnostic interview with a biological parent, and confirmatory factor analysis was used to model distress and fear dimensions.ResultsParental distress risk was associated with an attenuated LPP to all stimuli. In contrast, parental fear risk was associated with an enhanced LPP to unpleasant pictures but was unrelated to the LPP to neutral and pleasant pictures. Furthermore, these results were independent of the adolescent girls’ current depression and anxiety symptoms and pubertal status.ConclusionsThe present study demonstrates that familial risk for distress and fear disorders may have unique profiles in terms of electrocortical measures of emotional information processing. This study is also one of the first to investigate emotional/motivational processes underlying the distress and fear disorder dimensions.

The Clinical Characteristics of Major Depression as Indices of the Familial Risk to Illness

1994 ◽  
Vol 165 (1) ◽  
pp. 66-72 ◽  
Author(s):  
Kenneth S. Kendler ◽  
Michael C. Neale ◽  
Ronald C. Kessler ◽  
Andrew C. Heath ◽  
Lindon J. Eaves
Keyword(s):  
Major Depression ◽  
Clinical Characteristics ◽  
Age At Onset ◽  
Familial Risk ◽  
Population Based ◽  
Treatment Seeking ◽  
Generalised Anxiety Disorder ◽  
Increased Risk ◽  
Co Morbidity ◽  
Familial Vulnerability

BackgroundFrom both a clinical and an aetiological perspective, major depression (MD) is probably a heterogeneous condition. We attempt to relate these two domains.MethodWe examined which of an extensive series of clinical characteristics in 646 female twins from a population-based register with a lifetime diagnosis of MD predicts the risk for MD in co-twins. MD was defined by DSM–III–R criteria.ResultsFour variables uniquely predicted an increased risk for MD in the co-twin: number of episodes, degree of impairment and co-morbidity with panic disorder or bulimia. One variable uniquely predicted decreased risk: co-morbidity with phobia. Variables that did not uniquely predict risk of MD in the co-twin included age at onset, number and kind of depressive symptoms, treatment seeking, duration of the longest episode and co-morbidity with generalised anxiety disorder and alcohol dependence.ConclusionsOur results suggest that the clinical features of MD can be meaningfully related to the familial vulnerability to illness, particularly with respect to recurrence, impairment and patterns of co-morbidity.

Reciprocal effects of stable and temporary components of neuroticism and affective disorders: results of a longitudinal cohort study

2013 ◽  
Vol 44 (2) ◽  
pp. 337-348 ◽  
Author(s):  
P. Spinhoven ◽  
E. Penelo ◽  
M. de Rooij ◽  
B. W. Penninx ◽  
J. Ormel
Keyword(s):  
Anxiety Disorder ◽  
Affective Disorders ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Prior History ◽  
Delayed Effect ◽  
Cross Sectional ◽  
Delayed Effects ◽  
Downward Spiral ◽  
History Of

BackgroundCross-sectional studies show that neuroticism is strongly associated with affective disorders. We investigated whether neuroticism and affective disorders mutually reinforce each other over time, setting off a potential downward spiral.MethodA total of 2981 adults aged 18–65 years, consisting of healthy controls, persons with a prior history of affective disorders and persons with a current affective disorder were assessed at baseline (T1) and 2 (T2) and 4 years (T3) later. At each wave, affective disorders according to DSM-IV criteria were assessed with the Composite Interview Diagnostic Instrument (CIDI) version 2.1 and neuroticism with the Neuroticism–Extraversion–Openness Five Factor Inventory (NEO-FFI).ResultsUsing structural equation models the association of distress disorders (i.e. dysthymia, depressive disorder, generalized anxiety disorder) and fear disorders (i.e. social anxiety disorder, panic disorder, agoraphobia without panic) with neuroticism could be attributed to three components: (a) a strong correlation of the stable components of distress and fear disorders with the stable trait component of neuroticism; (b) a modest contemporaneous association of change in distress and fear disorders with change in neuroticism; (c) a small to modest delayed effect of change in distress and fear disorders on change in neuroticism. Moreover, neuroticism scores in participants newly affected at T2 but remitted at T3 did not differ from their pre-morbid scores at T1.ConclusionsOur results do not support a positive feedback cycle of changes in psychopathology and changes in neuroticism. In the context of a relative stability of neuroticism and affective disorders, only modest contemporaneous and small to modest delayed effects of psychopathology on neuroticism were observed.

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