Latent class analysis of IPOs in the Nordics
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ABSTRACT:
SUBMITTER: Bask M
PROVIDER: S-EPMC8565723 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Correct and, hence, valid classifications of individuals are of high importance in the social sciences as these classifications are the basis for diagnoses and/or the assignment to a treatment. The via regia to inspect the validity of psychological ratings is the multitrait-multimethod (MTMM) approach. First, a latent variable model for the analysis of rater agreement (latent rater agreement model) will be presented that allows for the analysis of convergent validity between different measurement approaches (e.g., raters). Models of rater agreement are transferred to the level of latent variables. Second, the latent rater agreement model will be extended to a more informative MTMM latent class model. This model allows for estimating (i) the convergence of ratings, (ii) method biases in terms of differential latent distributions of raters and differential associations of categorizations within raters (specific rater bias), and (iii) the distinguishability of categories indicating if categories are satisfyingly distinct from each other. Finally, an empirical application is presented to exemplify the interpretation of the MTMM latent class model.
| S-EPMC4584970 | biostudies-literature
Project description:This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.
| S-EPMC6364555 | biostudies-literature
Project description:BackgroundLack of a gold standard for latent TB infection has precluded direct measurement of test characteristics of the tuberculin skin test and interferon-γ release assays (QuantiFERON Gold In-Tube and T-SPOT.TB).ObjectiveWe estimated test sensitivity/specificity and latent TB infection prevalence in a prospective, US-based cohort of 10 740 participants at high risk for latent infection.MethodsBayesian latent class analysis was used to estimate test sensitivity/specificity and latent TB infection prevalence among subgroups based on age, foreign birth outside the USA and HIV infection.ResultsLatent TB infection prevalence varied from 4.0% among foreign-born, HIV-seronegative persons aged <5 years to 34.0% among foreign-born, HIV-seronegative persons aged ≥5 years. Test sensitivity ranged from 45.8% for the T-SPOT.TB among foreign-born, HIV-seropositive persons aged ≥5 years to 80.7% for the tuberculin skin test among foreign-born, HIV-seronegative persons aged ≥5 years. The skin test was less specific than either interferon-γ release assay, particularly among foreign-born populations (eg, the skin test had 70.0% specificity among foreign-born, HIV-seronegative persons aged ≥5 years vs 98.5% and 99.3% specificity for the QuantiFERON and T-SPOT.TB, respectively). The tuberculin skin test's positive predictive value ranged from 10.0% among foreign-born children aged <5 years to 69.2% among foreign-born, HIV-seropositive persons aged ≥5 years; the positive predictive values of the QuantiFERON (41.4%) and T-SPOT.TB (77.5%) were also low among US-born, HIV-seropositive persons aged ≥5 years.ConclusionsThese data reinforce guidelines preferring interferon-γ release assays for foreign-born populations and recommending against screening populations at low risk for latent TB infection.Trial registration numberNCT01622140.
| S-EPMC6494673 | biostudies-literature
Project description:Sweden has witnessed an increase in the rates of sexual crimes including rape. Knowledge of who the offenders of these crimes are is therefore of importance for prevention. We aimed to study characteristics of individuals convicted of rape, aggravated rape, attempted rape or attempted aggravated rape (abbreviated rape+), against a woman ≥18 years of age, in Sweden. By using information from the Swedish Crime Register, offenders between 15 and 60 years old convicted of rape+ between 2000 and 2015 were included. Information on substance use disorders, previous criminality and psychiatric disorders were retrieved from Swedish population-based registers, and Latent Class Analysis (LCA) was used to identify classes of rape+ offenders. A total of 3 039 offenders were included in the analysis. A majority of them were immigrants (n = 1 800; 59.2%) of which a majority (n = 1 451; 47.7%) were born outside of Sweden. The LCA identified two classes: Class A - low offending class (LOC), and Class B - high offending class (HOC). While offenders in the LOC had low rates of previous criminality, psychiatric disorders and substance use disorders, those included in the HOC had high rates of previous criminality, psychiatric disorders and substance use disorders. While HOC may be composed by more "traditional" criminals probably known by the police, the LOC may represent individuals not previously known by the police. These two separated classes, as well as our finding in regard to a majority of the offenders being immigrants, warrants further studies that take into account the contextual characteristics among these offenders. Key pointsRape, aggravated rape, attempted rape or attempted aggravated rape (rape+) are increasing in Sweden.The majority of those convicted of rape+ are immigrants.LCA identifies two classes of rape+ offenders: LOC and HOC.
| S-EPMC8330751 | biostudies-literature
Project description:BACKGROUND:Maternal depression in the postpartum period confers substantial morbidity and mortality, but the definition of postpartum depression remains controversial. We investigated the heterogeneity of symptoms with the aim of identifying clinical subtypes of postpartum depression. METHODS:Data were aggregated from the international perinatal psychiatry consortium Postpartum Depression: Action Towards Causes and Treatment, which represents 19 institutions in seven countries. 17,912 unique subject records with phenotypic data were submitted. We applied latent class analyses in a two-tiered approach to assess the validity of empirically defined subtypes of postpartum depression. Tier one assessed heterogeneity in women with complete data on the Edinburgh postnatal depression scale (EPDS) and tier two in those with postpartum depression case status. FINDINGS:6556 individuals were assessed in tier one and 4245 in tier two. A final model with three latent classes was optimum for both tiers. The most striking characteristics associated with postpartum depression were severity, timing of onset, comorbid anxiety, and suicidal ideation. Women in class 1 had the least severe symptoms (mean EPDS score 10·5), followed by those in class 2 (mean EPDS score 14·8) and those in class 3 (mean EPDS score 20·1). The most severe symptoms of postpartum depression were significantly associated with poor mood (mean EPDS score 20·1), increased anxiety, onset of symptoms during pregnancy, obstetric complications, and suicidal ideation. In class 2, most women (62%) reported symptom onset within 4 weeks postpartum and had more pregnancy complications than in other two classes (69% vs 67% in class 1 and 29% in class 3). INTERPRETATION:PPD seems to have several distinct phenotypes. Further assessment of PPD heterogeneity to identify more precise phenotypes will be important for future biological and genetic investigations. FUNDING:Sources of funding are listed at the end of the article.
| S-EPMC4800818 | biostudies-literature
Project description:Latent Class Cluster Analysis (LCCA) is an advanced model-based clustering method, which is increasingly used in social, psychological, and educational research. Selecting the number of clusters in LCCA is a challenging task involving inevitable subjectivity of analytical choices. Researchers often rely excessively on fit indices, as model fit is the main selection criterion in model-based clustering; it was shown, however, that a wider spectrum of criteria needs to be taken into account. In this paper, we suggest an extended analytical strategy for selecting the number of clusters in LCCA based on model fit, cluster separation, and stability of partitions. The suggested procedure is illustrated on simulated data and a real world dataset from the International Computer and Information Literacy Study (ICILS) 2018. For the latter, we provide an example of end-to-end LCCA including data preprocessing. The researcher can use our R script to conduct LCCA in a few easily reproducible steps, or implement the strategy with any other software suitable for clustering. We show that the extended strategy, in comparison to fit indices-based strategy, facilitates the selection of more stable and well-separated clusters in the data. • The suggested strategy aids researchers to select the number of clusters in LCCA • It is based on model fit, cluster separation, and stability of partitions • The strategy is useful for finding separable generalizable clusters in the data.
| S-EPMC9192797 | biostudies-literature
Project description:To explore the latent classes of stigma in patients with rheumatoid arthritis, we analyzed the characteristics of the different categories. Adopting a convenient sampling method, socio-demographic and disease-related information from the outpatient clinics and wards of 3 tertiary care hospitals in China was collected. The Chinese version of the Internalized Stigma of Mental Illness scale-Rheumatoid Arthritis was used in this survey. Rheumatoid arthritis stigma was divided into 3 potential categories: Low Stigma-Strong Resistance (83, 41.5%), Medium Stigma-Strong Alienation (78, 39.0%), and High Stigma-Weak Resistance (39, 19.5%). Unordered multinomial logistic regression analysis showed that pain (OR = 1.540, P = .005; OR = 1.797, P < .001), elementary school education and below (OR = 4.051, P = .037), and duration of morning stiffness (OR = 0.267, P = .032) were risk factors for stigma, whereas family history was a protective factor against stigma (OR = 0.321, P = .046). Patients with longer morning stiffness, more severe pain, and less education have a greater risk of heavier stigma. Strong alienation is an early warning of heavy stigma. Resistance to stigma and family support can help patients overcome their psychological obstacles. More attention should be paid to constructing family centered support systems to help resist stigma.
| S-EPMC10256359 | biostudies-literature
Project description:Accurate diagnosis and subsequent treatment of latent tuberculosis infection (LTBI) is essential for TB elimination. However, the absence of a gold standard test for diagnosing LTBI makes assessment of the true prevalence of LTBI and the accuracy of diagnostic tests challenging. Bayesian latent class models can be used to make inferences about disease prevalence and the sensitivity and specificity of diagnostic tests using data on the concordance between tests. We performed the largest meta-analysis to date aiming to evaluate the performance of tuberculin skin test (TST) and interferon-gamma release assays (IGRAs) for LTBI diagnosis in various patient populations using Bayesian latent class modelling.Systematic search of PubMeb, Embase and African Index Medicus was conducted without date and language restrictions on September 11, 2017 to identify studies that compared the performance of TST and IGRAs for LTBI diagnosis. Two IGRA methods were considered: QuantiFERON-TB Gold In Tube (QFT-GIT) and T-SPOT.TB. Studies were included if they reported 2x2 agreement data between TST and QFT-GIT or T-SPOT.TB. A Bayesian latent class model was developed to estimate the sensitivity and specificity of TST and IGRAs in various populations, including immune-competent adults, immune-compromised adults and children. A TST cut-off value of 10 mm was used for immune-competent subjects and 5 mm for immune-compromised individuals.A total of 157 studies were included in the analysis. In immune-competent adults, the sensitivity of TST and QFT-GIT were estimated to be 84% (95% credible interval [CrI] 82-85%) and 52% (50-53%), respectively. The specificity of QFT-GIT was 97% (96-97%) in non-BCG-vaccinated and 93% (92-94%) in BCG-vaccinated immune-competent adults. The estimated figures for TST were 100% (99-100%) and 79% (76-82%), respectively. T-SPOT.TB has comparable specificity (97% for both tests) and better sensitivity (68% versus 52%) than QFT-GIT in immune-competent adults. In immune-compromised adults, both TST and QFT-GIT display low sensitivity but high specificity. QFT-GIT and TST are equally specific (98% for both tests) in non-BCG-vaccinated children; however, QFT-GIT is more specific than TST (98% versus 82%) in BCG-vaccinated group. TST is more sensitive than QFT-GIT (82% versus 73%) in children.This study is the first to assess the utility of TST and IGRAs for LTBI diagnosis in different population groups using all available data with Bayesian latent class modelling. Our results challenge the current beliefs about the performance of LTBI screening tests, and have important implications for LTBI screening policy and practice. We estimated that the performance of IGRAs is not as reliable as previously measured in the general population. However, IGRAs are not or minimally affected by BCG and should be the preferred tests in this setting. Adoption of IGRAs in settings where BCG is widely administered will allow for a more accurate identification and treatment of LTBI.
| S-EPMC5705142 | biostudies-literature
Project description:BackgroundHealth-related quality of life (HRQOL) heterogeneity among cancer survivors may mask subgroups (classes) with different limitations and long-term outcomes. The authors determined the HRQOL classes that exist among lung cancer survivors, examined transitions among those classes over time, and compared survival outcomes of patients according to the classes present in the initial phase of care.MethodsLung cancer survivors in the Cancer Care Outcomes Research and Surveillance Consortium completed EuroQol 5-domain quality-of-life questionnaires 4.8 months (initial phase) and >1 year (survivorship phase) after diagnosis (n = 1396). Latent class analysis and latent transition analysis were used to determine HRQOL classes and transitions across time. Correlates of class membership were tested using multinomial logistic regression. Kaplan-Meier and Cox regression analyses were used to compare survival across class membership.ResultsLatent class analysis identified 4 classes at diagnosis and follow-up: 1) poor HRQOL, 2) pain-dominant impairment, 3) mobility/usual activities impairment, and 4) good HRQOL. Probabilities of remaining in the same class were .87, .85, .82, and .73 for classes 4, 1, 3, and 2, respectively. Younger age, lower income, lower education, comorbidities, and a history of depression/emotional problems were associated with a greater likelihood of being in classes 1, 2, or 3 at follow-up. Patients in classes 1 and 3 had significantly lower median survival estimates than patients in class 4 (4.8 years, 3.8 years, and 5.5 years, respectively; P < .001).ConclusionsExamining the heterogeneity of HRQOL in lung cancer populations allows the identification of classes with different limitations and long-term outcomes and, thus, guides tailored and patient-centered provision of supportive care.
| S-EPMC4409523 | biostudies-literature
Project description:OBJECTIVE:Many children diagnosed with attention-deficit/hyperactivity disorder (ADHD) experience co-occurring neurodevelopmental and psychiatric disorders, and those who do often exhibit higher levels of impairment than children with ADHD alone. This study provides a latent class analysis (LCA) approach to categorizing children with ADHD into comorbidity groups, evaluating condition expression and treatment patterns in each group. METHODS:Parent-reported data from a large probability-based national sample of children diagnosed with ADHD (2014 National Survey of the Diagnosis and Treatment of ADHD and Tourette Syndrome) were used for an LCA to identify groups of children with similar groupings of neurodevelopmental and psychiatric comorbidities among children with current ADHD (n = 2495). Differences between classes were compared using multivariate logistic regressions. RESULTS:LCA placed children who were indicated to have ADHD into 4 classes: (low comorbidity [LCM] [64.5%], predominantly developmental disorders [PDD] [13.7%], predominantly internalizing disorders [PID] [18.5%], and high comorbidity [HCM] [3.3%]). Children belonging to the HCM class were most likely to have a combined ADHD subtype and the highest number of impaired domains. Children belonging to the PDD class were most likely to be receiving school services, whereas children in the PID class were more likely to be taking medication than those belonging to the LCM class who were least likely to receive psychosocial treatments. CONCLUSION:Latent classes based on co-occurring psychiatric conditions predicted use of varied treatments. These findings contribute to the characterization of the ADHD phenotype and may help clinicians identify how services could be best organized and coordinated in treating ADHD.
| S-EPMC5747544 | biostudies-literature