Latent classification model for censored longitudinal binary outcome.

Latent classification model is a class of statistical methods for identifying unobserved class membership among the study samples using some observed data. In this study, we proposed a latent classification model that takes a censored longitudinal binary outcome variable and uses its changing pattern over time to predict individuals' latent class membership. Assuming the time-dependent outcome variables follow a continuous-time Markov chain, the proposed method has two primary goals: (1) estimate the distribution of the latent classes and predict individuals' class membership, and (2) estimate the class-specific transition rates and rate ratios. To assess the model's performance, we conducted a simulation study and verified that our algorithm produces accurate model estimates (ie, small bias) with reasonable confidence intervals (ie, achieving approximately 95% coverage probability). Furthermore, we compared our model to four other existing latent class models and demonstrated that our approach yields higher prediction accuracies for latent classes. We applied our proposed method to analyze the COVID-19 data in Houston, Texas, US collected between January first 2021 and December 31st 2021. Early reports on the COVID-19 pandemic showed that the severity of a SARS-CoV-2 infection tends to vary greatly by cases. We found that while demographic characteristics explain some of the differences in individuals' experience with COVID-19, some unaccounted-for latent variables were associated with the disease.

Identification of the health education targeted susceptible population of tuberculosis in Ningxia, Northwest China.

Knowledge, Attitude, and Practice (KAP) survey, as an effective measure tool, is of practical significance for identifying the susceptible population in high-incidence regions of tuberculosis (TB). We aim to identify the health education targeted susceptible population of TB and discuss the acting pathway of KAP in Ningxia. A multistage random sampling method was used to conduct a face-to-face questionnaire survey for residents. The latent class analysis (LCA) model was used to classify susceptible populations of TB, and the structural equation modeling (SEM) model was also employed to investigate the interaction mechanisms of KAP (mediation analysis). We further applied the ordered logistic regression model to explore the associated factors. A total of 973 residents were enrolled, 70.6% were male, aged from 16 to 89. The LCA analysis demonstrated that 3 categories of susceptible populations of TB ("overall good", "positive attitude" and "overall poor") have optimal goodness of fit (BIC = 7889.5, Entropy = 0.923). SEM model indicated that the attitude plays a significant mediation effect from knowledge to practice toward TB (an indirect effect of 0.038, and a direct effect of 0.138). The ordered logistic regression results found that age, sex, marital status, education level, occupation, family income, self-perceived health status, having a family member or friend with TB, and knowing the DOTS strategy were significantly associated with classifications of KAP level towards TB. Based on the LCA model, we accurately classified the susceptible population of TB into 3 groups with different degrees of KAP. We found that TB attitude plays a mediating role between knowledge and practice. Therefore, we should pay more attention and carry out targeted health education in the community to these populations with overall poor KAP towards TB, and develop effective strategies and measures to realize the End TB Plan.

Patterns of Health-Risk Behaviours and Their Associations With Anxiety and Depression Among Chinese Young Adults by Gender: A Latent Class Analysis.

This study investigated gender differences in health-risk behaviour patterns among young adults and assessed the associations of anxiety and depression with these patterns. A cross-sectional survey was conducted with 1740 young Chinese adults aged 18-24 years. Latent class analysis (LCA) and multinomial logistic regression were conducted to identify the clusters of health-risk behaviours and their associations with anxiety and depression. Three common patterns were found for both genders: physical inactivity, substance use, and insufficient fruit intake (5.7% for males [M] and 11.6% for females [F]); a sedentary lifestyle only (48.4% for M and 48.9% for F); and a sedentary lifestyle, substance use, and an unhealthy diet (7.6% for M and 20.0% for F). Additionally, two additional unique patterns were found: physical inactivity and unhealthy diet in males (38.3%) and physical inactivity and insufficient fruit intake in females (19.6%). Sociodemographic variables exert different effects on health-risk behaviour patterns as a function of gender. Lower anxiety levels (odds ratio [OR]: 0.892; 95% confidence interval [CI]: 0.823-0.966) and greater depression levels (OR: 1.074; 95% CI: 1.008-1.143) were associated with a sedentary lifestyle, substance use, and unhealthy diet class only in female young adults compared with a sedentary-only class. These findings underscore the need for the implementation of targeted interventions based on gender differences.

Using Spelling Error Analyses to Examine Individual Differences in German Students From Diverse Linguistic Backgrounds: A Latent Class Approach.

The current study examined German spelling errors among students with German as their first language (L1) and those with German as their second language (L2) in Grades 3-4 (elementary school students; n = 127) and Grades 5-7 (secondary school students; n = 379). Five hundred and six students participated in the study. We performed two separate latent class analyses on elementary and secondary school students. Results indicate that elementary school students can be categorized as good (Class 1), consonant error dominant (Class 2), or poor (Class 3) spellers. However, secondary students can be categorized as addition and sequence error dominant (Class 1), substitution and omission error dominant (Class 2), or poor (Class 3) spellers. The three-step multinomial logistic regression analyses suggested that decoding was associated with the highest chances of being poor spellers in both elementary and secondary schools. Speaking German as L1 or L2 was a significant predictor of heterogeneities in secondary, but not elementary, school students. Polish L1 secondary students had the highest possibility of being poor spellers. The results suggest heterogeneities of student profiles. In addition, special attention should be given to secondary school students with the Polish L1 background in their spelling struggles associated with German orthography.

An Alternative Way to Model Population Ability Distributions in Large-Scale Educational Surveys.

In large-scale educational surveys, a latent regression model is used to compensate for the shortage of cognitive information. Conventionally, the covariates in the latent regression model are principal components extracted from background data. This operational method has several important disadvantages, such as the handling of missing data and the high model complexity. The approach introduced here to identify multiple groups that can account for the variation among students is to conduct a latent class analysis (LCA). In the LCA, one or more latent nominal variables are identified that can be used to classify respondents with respect to their background characteristics. These classifications are then introduced as predictors in the latent regression. The primary goal of this study was to explore whether this approach yields similar estimates of group means and standard deviations compared with the operational procedure. The alternative approaches based on LCA differed regarding the number of classes, the items used for the LCA, and whether manifest class membership information or class membership probabilities were used as independent variables in the latent regression. Overall, recovery of the operational approach's group means and standard deviations was very satisfactory for all LCA approaches. Furthermore, the posterior means and standard deviations used to generate plausible values derived from the operational approach and the LCA approaches correlated highly. Thus, incorporating independent variables based on an LCA of background data into the latent regression model appears to be a viable alternative to the operational approach.