Multimethod latent class analysis.
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SUBMITTER: Nussbeck FW
PROVIDER: S-EPMC4584970 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Publications
Multimethod latent class analysis.
Nussbeck Fridtjof W FW Eid Michael M
Frontiers in psychology 20150917
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 measuremen ...[more]
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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.
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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.
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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.
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