Seeking knowledge or efficiency: Profiling students’ AI-use through survey-based latent class analysis

This study examined empirically derived symptom cluster profiles among patients who present with insomnia using clinical data and polysomnography. Latent profile analysis was used to identify symptom cluster profiles of 175 individuals (63% female) with insomnia disorder based on total scores on validated self-report instruments of daytime and nighttime symptoms (Insomnia Severity Index, Glasgow Sleep Effort Scale, Fatigue Severity Scale, Beliefs and Attitudes about Sleep, Epworth Sleepiness Scale, Pre-Sleep Arousal Scale), mean values from a 7-day sleep diary (sleep onset latency, wake after sleep onset, and sleep efficiency), and total sleep time derived from an in-laboratory PSG. The best-fitting model had three symptom cluster profiles: "High Subjective Wakefulness" (HSW), "Mild Insomnia" (MI) and "Insomnia-Related Distress" (IRD). The HSW symptom cluster profile (26.3% of the sample) reported high wake after sleep onset, high sleep onset latency, and low sleep efficiency. Despite relatively comparable PSG-derived total sleep time, they reported greater levels of daytime sleepiness. The MI symptom cluster profile (45.1%) reported the least disturbance in the sleep diary and questionnaires and had the highest sleep efficiency. The IRD symptom cluster profile (28.6%) reported the highest mean scores on the insomnia-related distress measures (eg, sleep effort and arousal) and waking correlates (fatigue). Covariates associated with symptom cluster membership were older age for the HSW profile, greater obstructive sleep apnea severity for the MI profile, and, when adjusting for obstructive sleep apnea severity, being overweight/obese for the IRD profile. The heterogeneous nature of insomnia disorder is captured by this data-driven approach to identify symptom cluster profiles. The adaptation of a symptom cluster-based approach could guide tailored patient-centered management of patients presenting with insomnia, and enhance patient care.

Purpose: Our objectives were to (a) identify oral and pharyngeal physiologic swallowing impairment severity classes based on latent class analyses (LCAs) of the Modified Barium Swallow Impairment Profile (MBSImP) swallow task scores and (b) quantify the probability of severity class membership given composite MBSImP oral total (OT) and pharyngeal total (PT) scores.Method: MBSImP scores were collected from a patient database of 319 consecutive modified barium swallow studies. Because of missing swallow task scores, LCA was performed using 25 multiply imputed data sets.Results: LCA revealed a three-class structure for both oral and pharyngeal models. We identified OT and PT score intervals to assign subjects to oral and pharyngeal impairment latent severity classes, respectively, with high probability (probability of class membership ≥ 0.9 given OT or PT scores within specified ranges) and high confidence (95% credible interval [CI] widths ≤ 0.24 for all total scores within specified ranges). OT scores ranging from 0 to 10 and from 14 to 18 yielded assignments in Oral Latent Classes 1 and 2, respectively, while OT = 22 was assigned to Oral Latent Class 3. PT scores ranging from 0 to 13 and from 18 to 24 yielded assignments in Pharyngeal Latent Classes 1 and 2, respectively, while PT = 26 was assigned to Pharyngeal Latent Class 3.Conclusions: LCA of MBSImP task-level data revealed significant underlying oral and pharyngeal ordinal class structures representing increasingly severe gradations of physiologic swallow impairment. Clinically meaningful OT and PT score ranges were derived facilitating latent class assignment.Supplemental Material S1. Summary of the mathematical details of the latent class model and the procedures for construction of all model-derived conditional probabilities.Beall, J., Hill, E. G., Armeson, K., (Focht) Garand, K. L., (Humphries) Davidson, K., & Martin-Harris, B. (2020). Classification of physiologic swallowing impairment severity: A latent class analysis of modified barium swallow impairment profile scores. American Journal of Speech-Language Pathology, 29(2S), 1001–1011. https://doi.org/10.1044/2020_AJSLP-19-00080Publisher Note: This article is part of the Special Issue: Select Papers From the 2018 Charleston Swallowing Conference at Northwestern University.

Using Latent Profile Analysis and Related Approaches in Adolescent Health Research

Latent Class and Factor Analysis of DSM-IV ADHD: A Twin Study of Female Adolescents

Increasing evidence suggests that amyloid reduction could serve as a plausible surrogate endpoint for clinical and cognitive efficacy. The double-blind phase 3 DIAN-TU-001 trial tested clinical and cognitive declines with increasing doses of solanezumab or gantenerumab. We used latent class (LC) analysis on data from the Dominantly Inherited Alzheimer Network Trials Unit 001 trial to test amyloid positron emission tomography (PET) reduction as a potential surrogate biomarker. LC analysis categorized participants into three classes: amyloid no change, amyloid reduction, and amyloid growth, based on longitudinal amyloid Pittsburgh compound B PET standardized uptake value ratio data. The amyloid-no-change class was at an earlier disease stage for amyloid amounts and dementia. Despite similar baseline characteristics, the amyloid-reduction class exhibited reductions in the annual decline rates compared to the amyloid-growth class across multiple biomarker, clinical, and cognitive outcomes. LC analysis indicates that amyloid reduction is associated with improved clinical outcomes and supports its use as a surrogate biomarker in clinical trials. We used latent class (LC) analysis to test amyloid reduction as a surrogate biomarker. Despite similar baseline characteristics, the amyloid-reduction class exhibited remarkably better outcomes compared to the amyloid-growth class across multiple measures. LC analysis proves valuable in testing amyloid reduction as a surrogate biomarker in clinical trials lacking significant treatment effects.

Cognitive variations following exposure to childhood adversity: evidence from a pre-registered, longitudinal study

Background Policies to tackle obesity have increasingly targeted drinks, in particular sugar-sweetened beverages (SSBs), as a major source of excess sugar and energy. However, precision targeting of policies is difficult as information on what types of consumers they might affect, and to what degree, is missing. To fill this gap, we categorised consumers on the basis of drink purchase behaviour and explored whether they are patterned by socio-demographic characteristics, total food purchasing and weight status. Methods We used latent class (LC) analysis to characterise patterns of drink purchases using the 2016 UK Kantar FMCG household purchase panel. We restricted analyses to frequent purchasers of drinks (n = 8,675) and used 6 drink categories: SSB; diet drink; fruit-/milk-based drinks; beer & cider; wine; and water. We used multinomial logistic and linear models to relate household characteristics, BMI and food purchase behaviours to LC membership. Results We identified 7 LCs. Disadvantaged households were more frequent in LCs with high volumes of SSBs ('SSB') and diet drinks ('Diet'). Higher BMI was more likely in LCs 'Diet' and 'SSB'. LC 'SSB' obtained higher total energy, relatively less energy from fruits and vegetables, and more energy from less healthy products, compared to others. LCs 'Diet' and 'SSB' obtained relatively more energy from sweet snacks. Conclusions Households who mainly purchased high volumes of SSBs or diet drinks were at greater risk of obesity and tended to purchase less healthy foods, including a high proportion of energy from sweet snacks. These households might additionally benefit from policies targeting unhealthy foods, such as sweet snacks, as a way of reducing excess energy intake. Key messages The effects of fiscal policies on SSB consumption is likely to vary across types of beverage consumers. Fiscal policies should be extended to sweet snacks as a major source of excess energy.

The basic idea underlying latent class (LC) analysis is a very simple one: some of the parameters of a postulated statistical model differ across unobserved subgroups. These subgroups form the categories of a categorical latent variable (see entry latent variable). This basic idea has several seemingly unrelated applications, the most important of which are clustering, scaling, density estimation, and random-effects modeling. Outside social sciences, LC models are often referred to as finite mixture models. LC analysis was introduced in 1950 by Lazarsfeld, who used the technique as a tool for building typologies (or clustering) based on dichotomous observed variables. More than 20 years later, Goodman (1974) made the model applicable in practice by developing an algorithm for obtaining maximum likelihood estimates of the model parameters. He also proposed extensions for polytomous manifest variables and multiple latent variables, and did important work on the issue of model identification. During the same period, Haberman (1979) showed the connection between LC models and log-linear models for frequency tables with missing (unknown) cell counts. Many important extensions of the classical LC model have been proposed since then, such as models containing (continuous) covariates, local dependencies, ordinal variables, several latent variables, and repeated measures. A general framework for categorical data analysis with discrete latent variables was proposed by Hagenaars (1990) and extended by Vermunt (1997). While in the social sciences LC and finite mixture models are conceived primarily as tools for categorical data analysis, they can be useful in several other areas as well. One of these is density estimation, in which one makes use of the fact that a complicated density can be approximated as a finite mixture of simpler densities. LC analysis can also be used as a probabilistic cluster analysis tool for continuous observed variables, an approach that offers many advantages over traditional cluster techniques such as K-means clustering (see latent profile model). Another application area is dealing with unobserved heterogeneity, for example, in regression analysis with dependent observations (see non-parametric random-effects model).

Background: The diagnosis of complex posttraumatic stress disorder (CPTSD) has been suggested for inclusion in the 11th version of the International Classification of Diseases (ICD-11), with support for its construct validity coming from studies employing Latent Class Analysis (LCA) and Latent Profile Analysis (LPA). Objective: The current study aimed to critically evaluate the application of the techniques LCA and LPA as applied in previous studies to substantiate the construct validity of CPTSD. Method: Both LCA and LPA were applied systematically in one sample (n = 245), replicating the setup of previous studies as closely as possible. The interpretation of classes was augmented with the use of graphical visualization. Results: The LCA and LPA analyses indicated divergent results in the same dataset. LCA and LPA partially supported the existence of classes of patients endorsing different PTSD and CPTSD symptom patterns. However, further inspection of the results with scatterplots did not support a clear distinction between PTSD and CPTSD, but rather suggested that there is much greater variability in clinical presentations amongst adult PTSD patients than can be fully accounted for by either PTSD or CPTSD. Discussion: We argue that LCA and LPA may not be sufficient methods to decide on the construct validity of CPTSD, as different subgroups of patients are identified, depending on the statistical exact method used and the interpretation of the fit of different models. Additional methods, including graphical inspection should be employed in future studies.

Traditionally latent class (LC) analysis is used by applied researchers as a tool for identifying substantively meaningful clusters. More recently, LC models have also been used as a density estimation tool for categorical variables. We introduce a divisive LC (DLC) model as a density estimation tool that may offer several advantages in comparison to a standard LC model. When using an LC model for density estimation, a considerable number of increasingly large LC models may have to be estimated before sufficient model-fit is achieved. A DLC model consists of a sequence of small LC models. Therefore, a DLC model can be estimated much faster and can easily utilize multiple processor cores, meaning that this model is more widely applicable and practical. In this study we describe the algorithm of fitting a DLC model, and discuss the various settings that indirectly influence the precision of a DLC model as a density estimation tool. These settings are illustrated using a synthetic data example, and the best performing algorithm is applied to a real-data example. The generated data example showed that, using specific decision rules, a DLC model is able to correctly model complex associations amongst categorical variables.

The main aim of the present study was to investigate the presence of Differential Item Functioning (DIF) using a latent class (LC) analysis approach. Particularly, we examined potential sources of DIF in relation to gender. Data came from 6,265 Saudi Arabia students, who completed a high-stakes standardized admission test for university entrance. The results from a Latent Class Analysis (LCA) revealed a three-class solution (i.e., high, average, and low scorers). Then, to better understand the nature of the emerging classes and the characteristics of the people who comprise them, we applied a new stepwise approach, using the Multiple Indicator Multiple Causes (MIMIC) model. The model identified both uniform and non-uniform DIF effects for several items across all scales of the test, although, for the majority of them, the DIF effect sizes were negligible. Findings from this study have important implications for both measurement quality and interpretation of the results. Particularly, results showed that gender is a potential source of DIF for latent class indicators; thus, it is important to include those direct effects in the latent class regression model, to obtain unbiased estimates not only for the measurement parameters but also of the structural parameters. Ignoring these effects might lead to misspecification of the latent classes in terms of both the size and the characteristics of each class, which in turn, could lead to misinterpretations of the obtained latent class results. Implications of the results for practice are discussed.

BackgroundThe prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrome is latent class analysis (LCA). One approach to causal inference in LCA is using propensity score (PS) methods. The aim of this study was to investigate the causal effect of smoking on latent hazard classes of metabolic syndrome using the method of latent class causal analysis.MethodsIn this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third phase (2005–2008) were included. Metabolic syndrome was evaluated as outcome and latent variable in LCA in the data of the fifth phase (2014–2015). The step-by-step procedure for conducting causal inference in LCA included: (1) PS estimation and evaluation of overlap, (2) calculation of inverse probability-of-treatment weighting (IPTW), (3) PS matching, (4) evaluating balance of confounding variables between exposure groups, and (5) conducting LCA using the weighted or matched data set.ResultsBased on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and the metabolic syndrome latent classes. PS matching which compared low and moderate risk classes compared to class without metabolic syndrome, showed that smoking increases the probability of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63). In the unadjusted analysis, smoking increased the chances of being in the low-risk (OR: 1.45; 95% CI: 1.01, 2.08) and moderate-risk (OR: 1.68; 95% CI: 1.18, 2.40) classes of metabolic syndrome compared to the class without metabolic syndrome.ConclusionsBased on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed between smoking and moderate-risk and high-risk classes of metabolic syndrome.

The goal of this study is to gauge the consistency of Attention Deficit/Hyperactivity Disorder (ADHD) latent class models that are generated by different informants such as adolescents and parents. The consistency of adolescent-derived latent classes from two different samples was assessed and these results were then compared to the class structure generated by parent-report ADHD information. Self-reported DSM-IV Criterion A ADHD symptoms of 497 adolescent males from a population-based twin study in the state of Missouri (USA) were subjected to principal components and latent class analysis, and findings were compared to previous results obtained from identical analyses using an adolescent sample from Porto Alegre, Brazil (N = 483). The bi-dimensional structure of self-reported ADHD symptoms was similar for both male adolescent groups, but explained less than 40% of the symptom variance in either sample. Two factors, one with loadings on inattention symptoms only and the other with loadings on hyperactive-impulsive symptoms only, were identified in the Missouri sample. Specific ADHD latent classes did not replicate well across the Missouri and Brazilian samples, and both groups were characterized by the presence of several combined symptom classes but few inattentive or hyperactive-impulsive classes. While adolescent-report information across two different cultures can at least in part reproduce the two-factor structure of ADHD, results from latent class analysis suggest that adolescent reporting on their own symptoms is markedly different from the type of information parents provide about ADHD symptoms in their offspring. The current findings indicate that if male adolescents endorse any ADHD symptoms there is a tendency for them to report combined type problems.

BackgroundAdolescents and young adults are at higher risk of acquiring Chlamydia trachomatis infection (chlamydia), so testing is promoted in these populations. Studies have shown that re-testing for chlamydia is common amongst them. We investigated how sexual risk behaviour profiles are associated with repeated testing for chlamydia.MethodsWe used baseline data from a cohort of 2814 individuals recruited at an urban STI -clinic. We applied latent class (LC) analysis using 9 manifest variables on sexual behaviour and substance use self-reported by the study participants. We fitted ordered logistic regression to investigate the association of LC membership with the outcomes repeated testing during the past 12 months and lifetime repeated testing for chlamydia. Models were fit separately for men and women.ResultsWe identified four LCs for men and three LCs for women with increasing gradient of risky sexual behaviour. The two classes with the highest risk among men were associated with lifetime repeated testing for chlamydia: adjOR = 2.26 (95%CI: 1.50–3.40) and adjOR = 3.03 (95%CI: 1.93–4.74) as compared with the class with lowest risk. In women, the class with the highest risk was associated with increased odds of repeated lifetime testing (adjOR =1.85 (95%CI: 1.24–2.76)) and repeated testing during past 12 months (adjOR = 1.72 (95%CI: 1.16–2.54)). An association with chlamydia positive test at the time of the study and during the participant’s lifetime was only found in the male highest risk classes.ConclusionPrevention messages with regard to testing for chlamydia after unprotected sexual contact with new/casual partners seem to reach individuals in highest risk behaviour classes who are more likely to test repeatedly. Further prevention efforts should involve potentially more tailored sex-specific interventions taking into consideration risk behaviour patterns.

This study uses independent latent class analysis (LCA) and latent transition analysis (LTA) to explore accurate diagnosis and disease status change of a big Alzheimer's disease Neuroimaging Initiative (ADNI) data of 2,132 individuals over a 3-year period. The data includes clinical and neural measures of controls (CN), individuals with subjective memory complains (SMC), early-onset mild cognitive impairment (EMCI), late-onset mild cognitive impairment (LMCI), and Alzheimer's disease (AD). LCA at each time point yielded 3 classes: Class 1 is mostly composed of individuals from CN, SMC, and EMCI groups; Class 2 represents individuals from LMCI and AD groups with improved scores on memory, clinical, and neural measures; in contrast, Class 3 represents LMCI and from AD individuals with deteriorated scores on memory, clinical, and neural measures. However, 63 individuals from Class 1 were diagnosed as AD patients. This could be misdiagnosis, as their conditional probability of belonging to Class 1 (0.65) was higher than that of Class 2 (0.27) and Class 3 (0.08). LTA results showed that individuals had a higher probability of staying in the same class over time with probability >0.90 for Class 1 and 3 and probability >0.85 for Class 2. Individuals from Class 2, however, transitioned to Class 1 from time 2 to time 3 with a probability of 0.10. Other transition probabilities were not significant. Lastly, further analysis showed that individuals in Class 2 who moved to Class 1 have different memory, clinical, and neural measures to other individuals in the same class. We acknowledge that the proposed framework is sophisticated and time-consuming. However, given the severe neurodegenerative nature of AD, we argue that clinicians should prioritize an accurate diagnosis. Our findings show that LCA can provide a more accurate prediction for classifying and identifying the progression of AD compared to traditional clinical cut-off measures on neuropsychological assessments.