scholarly journals Identification of developmental trajectory classes: Comparing three latent class methods using simulated and real data

2019 ◽  
Vol 42 ◽  
pp. 100288 ◽  
Author(s):  
Jitske J. Sijbrandij ◽  
Tialda Hoekstra ◽  
Josué Almansa ◽  
Sijmen A. Reijneveld ◽  
Ute Bültmann
Keyword(s):  
Latent Class ◽  
Real Data ◽  
Developmental Trajectory ◽  
Latent Class Methods

Developmental trajectory and gender differences in Chinese adolescents' physical and relational aggression: an analysis using the latent class growth model

2014 ◽  
Vol 6 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Chunyong Yuan ◽  
Aihui Shao ◽  
Xinyin Chen ◽  
Tao Xin ◽  
Li Wang ◽  
...  
Keyword(s):  
Gender Differences ◽  
Relational Aggression ◽  
Physical Aggression ◽  
Latent Class ◽  
Initial Level ◽  
Interpersonal Relationship ◽  
Developmental Trajectory ◽  
Content Type ◽  
Group A ◽  
Class Play

Purpose – The purpose of this paper is to investigate the developmental trajectory and patterns of physical aggression and relational aggression over time, and also to examine the gender differences of the three-year developmental process as well as the impact of the developmental trajectory on mental health. Design/methodology/approach – Participants: the participants of this study were newly enrolled junior school students. The study spanned three years with continuous tracking performed once every other year. Measures: class play questionnaire. Aggressive behaviors were measured by an adaptive Chinese version of the revised class play assessment. Statistical analysis: to address the questions of the present study, the latent class growth model (LCGM) was used to analyze the three-year longitudinal data by Mplus 6.1. Findings – The initial level of physical aggression in boys was higher than that in girls. There were three types of developmental trajectory for boys, corresponding to a lower initial level-increasing group, a middle initial level-increasing group and a higher initial level-stable group. However, girls demonstrated different patterns, corresponding to a lower initial level-increasing group, a middle initial level-increasing group and a higher initial level-decreasing group. In contrast to the physical aggression, the initial level of relational aggression in boys was lower than that in girls. There were four types of developmental trajectory for boys, corresponding to a lower initial level-increasing group, a middle initial level-increasing group, a middle initial level-declining group and a higher initial level-declining group. Girls illustrated different patterns, corresponding to a lower initial level-stable group, a middle initial level-increasing group and a higher initial level-declining group. Different developmental trajectory of physical and relational aggression would influence the interpersonal relationship. Originality/value – This paper used a person-centered latent variable approach instead of the variable-centered approach to investigate the developmental trajectory and patterns of physical aggression and relational aggression over three year by employing the LCGM. The initial level of physical aggression in boys was higher than that in girls. In contrast, the initial level of relational aggression in boys was lower than that in girls. There were gender differences in the pattern of physical and relational aggression development trajectory. Different developmental trajectory of physical and relational aggression would influence the interpersonal relationship.

scholarly journals Evaluating and optimizing the use of diagnostics during epidemics: Application to the 2017 plague outbreak in Madagascar

2021 ◽  
Author(s):  
Quirine Bosch ◽  
Voahangy Andrianaivoarimanana ◽  
Beza Ramasindrazana ◽  
Guillain Mikaty ◽  
Rado JL Rakotonanahary ◽  
...  
Keyword(s):  
Yersinia Pestis ◽  
Test Performance ◽  
Diagnostic Tests ◽  
Latent Class ◽  
Clinical Presentation ◽  
Analytical Framework ◽  
Rural Settings ◽  
Latent Class Methods ◽  
Diagnostic Outcomes ◽  
Case Classification

During outbreaks, the lack of diagnostic “gold standard” can mask the true burden of infection in the population and hamper the allocation of resources required for control. Here, we present an analytical framework to evaluate and optimize the use of diagnostics when multiple yet imperfect diagnostic tests are available. We apply it to laboratory results of 2,136 samples, analyzed with three diagnostic tests (based on up to seven diagnostic outcomes), collected during the 2017 pneumonic (PP) and bubonic plague (BP) outbreak in Madagascar, which was unprecedented both in the number of notified cases, clinical presentation, and spatial distribution. The extent of this outbreaks has however remained unclear due to non-optimal assays. Using latent class methods, we estimate that 7%-15% of notified cases were Yersinia pestis-infected. Overreporting was highest during the peak of the outbreak and lowest in the rural settings endemic to Yersinia pestis. Molecular biology methods offered the best compromise between sensitivity and specificity. The specificity of the rapid diagnostic test was relatively low (PP: 82%, BP: 85%), particularly for use in contexts with large quantities of misclassified cases. Comparison with data from a subsequent seasonal Yersinia pestis outbreak in 2018 reveal better test performance (BP: specificity 99%, sensitivity: 91%), indicating that factors related to the response to a large, explosive outbreak may well have affected test performance. We used our framework to optimize the case classification and derive consolidated epidemic trends. Our approach may help reduce uncertainties in other outbreaks where diagnostics are imperfect.

scholarly journals Early socioemotional competence, psychopathology, and latent class profiles of reparative prosocial behaviors from preschool through early adolescence

2019 ◽  
Vol 32 (2) ◽  
pp. 573-585 ◽  
Author(s):  
Meghan Rose Donohue ◽  
Rebecca Tillman ◽  
Joan Luby
Keyword(s):  
Early Adolescence ◽  
Latent Class ◽  
Psychiatric Symptoms ◽  
Growth Mixture Modeling ◽  
Emotion Understanding ◽  
Developmental Trajectory ◽  
Data Set ◽  
Preschool Period ◽  
Oppositional Defiant ◽  
Wide Range

AbstractChildren who have difficulty using reparative behaviors following transgressions display a wide range of poorer social and emotional outcomes. Despite the importance of reparative skills, no study has charted the developmental trajectory of these behaviors or pinpointed predictors of poorer reparative abilities. To address these gaps in the literature, this study applied growth mixture modeling to parent reports of children's reparative behaviors (N = 230) in a 9-year longitudinal data set spanning from preschool to early adolescence. Three distinct trajectories of reparative behaviors were found: a low-stable, moderate-stable, and high-stable latent class. Poorer emotion understanding, social withdrawal, social rejection, and maladaptive guilt in the preschool period predicted membership in a low-stable reparative trajectory. Externalizing diagnoses, particularly conduct disorder and oppositional defiant disorder, also predicted membership in a low-stable reparative trajectory. Preschool-onset depression predicted membership in a low-stable reparative trajectory through high levels of maladaptive guilt. The findings from this study suggest that socioemotional deficits in the preschool period set children on longstanding trajectories of impaired reparative responding. Thus, emotion understanding, social functioning, maladaptive guilt, and early psychiatric symptoms should be targeted in early preventive interventions.

Visualizing Latent Class Models with Analysis-of-distance BIPLOTS

2017 ◽  
Vol 47 (1) ◽  
pp. 345-378 ◽  
Author(s):  
Zsuzsa Bakk ◽  
Niel J. le Roux
Keyword(s):  
Relative Density ◽  
Latent Class ◽  
Real Data ◽  
Latent Class Models ◽  
Three Dimensions ◽  
Categorical Analysis ◽  
Class Models

The authors propose using categorical analysis-of-distance biplots to visualize the posterior classifications arising from a latent class (LC) model. Using this multivariate plot, it is possible to visualize in two (or three) dimensions the profile of multiple LCs, specifically both the within- and between-class variation, and the overlap or separation of the classes together with the class weights. Furthermore, visualization of the relative density of each of the data patterns associated with a class is possible. The authors illustrate this approach with real data examples of LC models with three and more classes.

Agreement on the Classification of Latent Class Membership Between Different Identification Constraint Approaches in the Mixture Rasch Model

Methodology ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 82-93
Author(s):  
Yi-Jhen Wu ◽  
Insu Paek
Keyword(s):  
Rasch Model ◽  
Latent Class ◽  
Model Identification ◽  
Real Data ◽  
Latent Classes ◽  
Class Membership ◽  
High Agreement ◽  
Common Scale ◽  
Constraint Approach

Abstract. When using the mixture Rasch model, the model identification constraints are either to set the equal means for all classes in the assumed normal ability distributions (equal ability mean constraint in short), or to set the sum of item difficulties to be zero for each class. In real data analysis, however, both constraints are not always sufficient to establish a common scale across latent classes unless some items are specified as anchor items in the estimation. If these two conventional constraint approaches recover the class membership as good as the anchor item constraint approach, the conventional constraint approaches may be considered useful for the purpose of class membership classification. This study investigated agreement on class membership between one conventional constraint (the equal ability mean) and the anchor item constraint approaches. Results showed high agreement between these two constraint approaches, indicating that the conventional constraint of the equal mean ability approach may be used to recover the latent class membership although item profiles are not correctly estimated across latent classes.

scholarly journals Behavioral and emotional dysregulation trajectories marked by prefrontal–amygdala function in symptomatic youth

2014 ◽  
Vol 44 (12) ◽  
pp. 2603-2615 ◽  
Author(s):  
M. A. Bertocci ◽  
G. Bebko ◽  
T. Olino ◽  
J. Fournier ◽  
A. K. Hinze ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Latent Class ◽  
Developmental Trajectories ◽  
Emotional Dysregulation ◽  
Developmental Trajectory ◽  
Healthy Controls ◽  
Longitudinal Assessment ◽  
Biological Targets ◽  
Prefrontal Cortical ◽  
Dorsolateral Prefrontal

BackgroundNeuroimaging measures of behavioral and emotional dysregulation can yield biomarkers denoting developmental trajectories of psychiatric pathology in youth. We aimed to identify functional abnormalities in emotion regulation (ER) neural circuitry associated with different behavioral and emotional dysregulation trajectories using latent class growth analysis (LCGA) and neuroimaging.MethodA total of 61 youth (9–17 years) from the Longitudinal Assessment of Manic Symptoms study, and 24 healthy control youth, completed an emotional face n-back ER task during scanning. LCGA was performed on 12 biannual reports completed over 5 years of the Parent General Behavior Inventory 10-Item Mania Scale (PGBI-10M), a parental report of the child's difficulty regulating positive mood and energy.ResultsThere were two latent classes of PGBI-10M trajectories: high and decreasing (HighD; n = 22) and low and decreasing (LowD; n = 39) course of behavioral and emotional dysregulation over the 12 time points. Task performance was >89% in all youth, but more accurate in healthy controls and LowD versus HighD (p < 0.001). During ER, LowD had greater activity than HighD and healthy controls in the dorsolateral prefrontal cortex, a key ER region, and greater functional connectivity than HighD between the amygdala and ventrolateral prefrontal cortex (p's < 0.001, corrected).ConclusionsPatterns of function in lateral prefrontal cortical–amygdala circuitry in youth denote the severity of the developmental trajectory of behavioral and emotional dysregulation over time, and may be biological targets to guide differential treatment and novel treatment development for different levels of behavioral and emotional dysregulation in youth.

Cancer patients’ trade-offs for efficacy, toxicity, and cost.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 6021-6021
Author(s):  
Yu-Ning Wong ◽  
Brian Egleston ◽  
Kush Sachdeva ◽  
Olivia Hamilton ◽  
Naa Eghan ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Cost Sharing ◽  
Latent Class ◽  
Individual Characteristics ◽  
Health Plans ◽  
Trade Offs ◽  
Costly Treatment ◽  
College Educated ◽  
Latent Class Methods ◽  
Out Of Pocket Costs

6021 Background: When making treatment decisions, cancer patients (pts) must make trade-offs between efficacy, toxicity (tox) and cost. However, little is known about how individual characteristics influence these decisions, particularly as many face high out of pocket costs. Methods: We presented cancer pts with hypothetical scenarios that asked them to choose between 2 treatments of varying levels of efficacy, tox and cost. Each scenario included 9 choice pairs. Pts were given 2 of 3 scenarios described in the Table. Tox was also varied. Demographics, cost concerns and numeracy were assessed. Within each scenario, we used latent class methods to distinguish pt groups with discrete preferences. We then used regressions with group membership probabilities as covariates to identify associations. Results: We enrolled 400 pts. Median age was 61 years (range 27-90). 63% were female. 41% were college educated. 51% had an annual income ≥$60K. 25% were enrolled at a community hospital. 98% were insured. Within each of the 3 scenarios, we identified 3 pt classes with preferences for survival or aversion to high cost or toxicity. Across each of the scenarios, <6% of pts in the group averse to high cost chose the costlier treatment. >92% of pts in the group that favored survival chose the highest efficacy treatment. >65% of pts in the group with aversion to tox chose the lower tox treatment. Within each of the scenarios, pts in the group with preference for survival were more likely to have an income of >$60K (p<.05) and greater numeracy skills (p<.05). In scenarios 2 and 3, pts with concerns about treatment costs were more likely to be in the class that was averse to high cost (p<.05 for both). Conclusions: Even in hypothetical scenarios presented to insured pts, socioeconomic status was predictive of treatment choice. Higher income pts may be more likely to focus on survival when making decisions while those with greater cost concerns may be more likely to avoid costly treatment, regardless of survival or tox. This raises the possibility that health plans with greater cost-sharing may have the unintended consequence of increasing disparities in care. [Table: see text]

scholarly journals Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data

2018 ◽  
Vol 43 (5) ◽  
pp. 511-539 ◽  
Author(s):  
Davide Vidotto ◽  
Jeroen K. Vermunt ◽  
Katrijn van Deun
Keyword(s):  
Multiple Imputation ◽  
Mixture Model ◽  
Categorical Data ◽  
Latent Class ◽  
Real Data ◽  
Latent Class Models ◽  
Parameter Estimates ◽  
Complex Interactions ◽  
Analysis Models ◽  
Class Models

With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex interactions in the joint distribution of the variables to be estimated. After formally introducing the model and showing how it can be implemented, we carry out a simulation study and a real-data study in order to assess its performance and compare it with the commonly used listwise deletion and an available R-routine. Results indicate that the BMLC model is able to recover unbiased parameter estimates of the analysis models considered in our studies, as well as to correctly reflect the uncertainty due to missing data, outperforming the competing methods.

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