Specifying and Interpreting Latent State–Trait Models With Autoregression: An Illustration

Prenoveau, Jason M.

doi:10.1080/10705511.2016.1186550

Cited by 49 publications

(72 citation statements)

References 34 publications

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“…The longitudinal measurement model of depressive symptoms without risk factors had good fit, RMSEA = 0.031, CFI = 0.988, and TLI = 0.984. Based on the variance decomposition described in Prenoveau (2016) [51], on average the overall factor representing the longitudinal persistence of depressive symptoms explained 38% of the model variance, whilst only 24% of variance was occasionspecific (eTable 1), suggesting that symptoms of depression are more stable than episodic in nature.…”

Section: Cortisol Sample Tso Model Of Depressive Symptomsmentioning

confidence: 99%

Persistent depressive symptoms, HPA-axis hyperactivity, and inflammation: the role of cognitive-affective and somatic symptoms

2019

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Hypothalamic-pituitary-adrenal (HPA)-axis hyperactivity and inflammation are thought to be prominent in the aetiology of depression. Although meta-analyses have confirmed this relationship, there is considerable variability in the effect sizes across studies. This could be attributed to a differential role of such biological systems in somatic versus cognitive-affective depressive symptoms which remains largely unexplored. Furthermore, most longitudinal research to date has focused on transient rather than persistent depressive symptoms. In the current study, we investigated the associations of hair cortisol and plasma C-reactive protein (CRP) with the longitudinal persistence and dimensions (cognitive-affective versus somatic) of depressive symptoms over a 14-year period using Trait-State-Occasion (TSO) structural equation modelling. The data came from a large sample of older adults from the English Longitudinal Study of Ageing. Depressive symptoms were assessed from wave 1 (2002-03) to wave 8 (2016-17). Hair cortisol (N = 4761) and plasma CRP (N = 5784) were measured in wave 6 (2012-13). Covariates included demographic, socioeconomic, lifestyle, chronic disease, and medication data. Our results revealed that higher cortisol and CRP levels were significantly associated with persistent depressive symptoms across the study period. Notably, both biomarkers exhibited stronger relationships with somatic than with cognitive-affective symptoms. The associations with somatic symptoms were also independent of relevant confounding factors. In contrast, their associations with cognitive-affective symptoms were weak after adjustment for all covariates. These distinct associations reveal the importance of considering symptom-specific effects in future studies on pathophysiological mechanisms. Ultimately, this will have the potential to advance the search for biomarkers of depression and facilitate more targeted treatments.

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Section: Cortisol Sample Tso Model Of Depressive Symptomsmentioning

confidence: 99%

Persistent depressive symptoms, HPA-axis hyperactivity, and inflammation: the role of cognitive-affective and somatic symptoms

2019

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“…Correlations between the 18 PGSs and phenotypic measures of substance abuse are displayed in Figure 2 and provided in eTable 5 (SI). The Trait-State-Occasion (TSO) model of substance abuse fits the data reasonably well (X 2 (42)=284.67, p<0.001, CFI=0.952, RMSEA=0.037, SRMR=0.058) 37 . On average, the common factor accounted for 22% of the total variance in the substance abuse scores.…”

Section: Resultsmentioning

confidence: 90%

“…All analyses were conducted in R version 3.5.1, using the 'Lavaan' package 35 . First, Trait-State-Occasion (TSO) structural equation models were fitted using the scores for cigarette, alcohol, cannabis, and other illicit substance abuse at each time point 36,37 . This approach enabled us to model latent factors of substance abuse that are stable over time, including (i) a common factor of all substances and (ii) substance-specific factors.…”

Section: Trait-state-occasion Models Of Substance Abusementioning

confidence: 99%

Identifying risk factors involved in the common versus specific liabilities to substance abuse: A genetically informed approach

Iob

Schoeler

Cecil

et al. 2019

Preprint

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The co-occurrence of abuse of multiple substances is thought to stem from a common liability that is partly genetic in origin. Genetic risk may indirectly contribute to a common liability through genetically influenced individual vulnerabilities and traits. To disentangle the aetiology of common versus specific liabilities to substance abuse, polygenic scores can be used as genetic proxies indexing such risk and protective individual vulnerabilities or traits. In this study, we used genomic data from a UK birth cohort study (ALSPAC, N=4218) to generate 18 polygenic scores indexing mental health vulnerabilities, personality traits, cognition, physical traits, and substance abuse. Common and substance-specific factors were identified based on four classes of substance abuse (alcohol, cigarettes, cannabis, other illicit substances) assessed over time (age 17, 20, and 22). In multivariable regressions, we then tested the independent contribution of selected polygenic scores to the common and substance-specific factors. Our findings implicated several genetically influenced traits and vulnerabilities in the common liability to substance abuse, most notably risk taking (bstandardized=0.14; 95%CI: 0.10,0.17), followed by extraversion (bstandardized =-0.10; 95%CI: -0.13,-0.06), and schizophrenia risk (bstandardized=0.06; 95%CI: 0.02;0.09). Educational attainment (EA) and body mass index (BMI) had opposite effects on substance-specific liabilities such as cigarettes (bstandardized-EA= -0.15; 95%CI: -0.19,-0.12; bstandardized-BMI=0.05; 95%CI: 0.02,0.09), alcohol (bstandardized-EA=0.07; 95%CI: 0.03,0.11; bstandardized-BMI= -0.06; 95%CI: -0.10,-0.02), and other illicit substances (bstandardized-EA=0.12; 95%CI: 0.07,0.17;bstandardized-BMI= -0.08; 95%CI:-0.13,-0.04). This is the first study based on genomic data that clarifies the aetiological architecture underlying the common versus substance-specific liabilities, providing novel insights for the prevention and treatment of substance abuse.

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“…Then, SEM was applied to the multiple imputed datasets using full information maximum likelihood. Several SEM models have been developed that allow separating stable trait variance from other sources of variance in a longitudinal repeated measures designs (Cole et al, 2005;Newsom, 2015;Prenoveau, 2016). We apply the TSO model with shared method variance factors (LaGrange & Cole, 2008), shown in Figure 1.…”

Section: Statistical Modeling Approachmentioning

confidence: 99%

“…A third component of the TSO model are the time-specific latent occasion factors (O1 to O9). They represent that portion of the relative position in leisure satisfaction at a moment in time (SSAT T ) that cannot be explained from knowing an individual's position on the leisure satisfaction trait (TSAT) (Prenoveau, 2016). In other words, they represent residual variation in the leisure satisfaction state factors once stable variance from the leisure satisfaction trait factor is accounted for 1 .…”

Section: Statistical Modeling Approachmentioning

confidence: 99%

How Stable is Leisure Satisfaction over Time? A Latent Trait-State-Occasion Analysis of Dutch Panel Data

Gelissen

2019

Leisure Sciences

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This study used a trait-state-occasion (TSO) model to isolate stable trait variance, occasion-specific state variance, and shared method related variance in a measure for leisure satisfaction in a Dutch nationally representative nine-year panel study. Findings indicate that satisfaction with leisure time is a consistently stronger indicator of overall leisure satisfaction than satisfaction with leisure activities. About half of the variance in leisure satisfaction is stable trait variance, with the remaining variance being mostly occasion-specific and to a lesser extent attributable to shared method variance and error. However, these findings depend on the age group we consider. Several socio-demographic variables relate directly to the trait aspect of leisure satisfaction. Our study underscores the importance of recognizing that over time leisure satisfaction measurements have considerable stable and more volatile elements and that one should control for shared method effects. ARTICLE HISTORY

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Specifying and Interpreting Latent State–Trait Models With Autoregression: An Illustration

Cited by 49 publications

References 34 publications

Persistent depressive symptoms, HPA-axis hyperactivity, and inflammation: the role of cognitive-affective and somatic symptoms

Persistent depressive symptoms, HPA-axis hyperactivity, and inflammation: the role of cognitive-affective and somatic symptoms

Identifying risk factors involved in the common versus specific liabilities to substance abuse: A genetically informed approach

How Stable is Leisure Satisfaction over Time? A Latent Trait-State-Occasion Analysis of Dutch Panel Data

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