Testing treatment effect in schizophrenia clinical trials with heavy patient dropout using latent class growth mixture models

2016 ◽  
Vol 15 (4) ◽  
pp. 349-361 ◽  
Author(s):  
Fanhui Kong ◽  
Yeh-Fong Chen
Keyword(s):  
Clinical Trials ◽  
Mixture Models ◽  
Treatment Effect ◽  
Latent Class ◽  
Growth Mixture Models ◽  
Patient Dropout ◽  
Growth Mixture ◽  
Heavy Patient ◽  
Latent Class Growth

scholarly journals development of deviant and delinquent behavior of adolescents

2006 ◽  
Vol 3 (1) ◽  
Author(s):  
Jost Reinecke
Keyword(s):  
Mixture Models ◽  
Latent Class ◽  
Unobserved Heterogeneity ◽  
Growth Mixture Modeling ◽  
Poisson Model ◽  
Continuous Variable ◽  
Individual Growth ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Latent Class Growth

The article presents applications of different growth mixture models considering unobserved heterogeneity within the framework of Mplus (Muthén and Muthén, 2001, 2004). Latent class growth mixture models are discussed under special consideration of count variables which can be incorporated into the mixture models via the Poisson and the zero-inflated Poisson model. Four-wave panel data from a German criminological youth study (Boers et al., 2002) is used for the model analyses. Three classes can be obtained from the data: Adolescents with almost no deviant and delinquent activities, a medium proportion of adolescents with a low increase of delinquency and a small number with a larger growth starting on a higher level. The best model fits are obtained with the zero-inflated Poisson model. Linear growth specifications are almost sufficient. The conditional application of the mixture models includes gender and educational level of the schools as time-independent predictors which are able to explain a large proportion of the latent class distribution. The stepwise procedure from latent class growth analysis to growth mixture modeling is feasible for longitudinal analyses where individual growth trajectories are heterogenous even when the dependent variable under study cannot be treated as a continuous variable.

Longitudinal Analysis of Adolescents’ Deviant and Delinquent Behavior

Methodology ◽  
2006 ◽  
Vol 2 (3) ◽  
pp. 100-112 ◽  
Author(s):  
Jost Reinecke
Keyword(s):  
Mixture Models ◽  
Latent Class ◽  
Unobserved Heterogeneity ◽  
Growth Mixture Modeling ◽  
Poisson Model ◽  
Continuous Variable ◽  
Individual Growth ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Latent Class Growth

This article presents applications of different growth mixture models considering unobserved heterogeneity within the framework of Mplus ( Muthén & Muthén, 2001a , 2001b , 2004 ). Latent class growth mixture models are discussed under special consideration of count variables that can be incorporated into the mixtures via the Poisson and the zero-inflated Poisson model. Fourwave panel data from a German criminological youth study (Boers et al., 2002) is used for the model analyses. Three classes can be obtained from the data: Adolescents with almost no deviant and delinquent activities, a medium proportion of adolescents with a low increase of delinquency, and a small number with a larger growth starting on a higher level. Considering the zero inflation of the data results in better model fits compared to the Poisson model only. Linear growth specifications are almost sufficient. The conditional application of the mixture models includes gender and educational level of the schools as time-independent predictors that are able to explain a large proportion of the latent class distribution. The stepwise procedure from latent class growth analysis to growth mixture modeling is feasible for longitudinal analyses where individual growth trajectories are heterogenous even when the dependent variable under study cannot be treated as a continuous variable.

scholarly journals Latent Class Trajectory and Growth Mixture Models in the Study of Physical Resilience

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 828-829
Author(s):  
Carl Pieper ◽  
Jane Pendergast ◽  
Megan Neely
Keyword(s):  
Mixture Models ◽  
Latent Class ◽  
Real Life ◽  
Individual Characteristics ◽  
Model Specification ◽  
Growth Mixture Models ◽  
Class Enumeration ◽  
Growth Mixture ◽  
Study Population ◽  
Number Of Classes

Abstract After a stressor, individuals may experience different trajectories of function and recovery. One potential explanation for this variation is differing trajectories may be indicators of differing classes or levels of resilience to the stressor. Latent Class Trajectory (LCTA) and Growth Mixture models (GMM) are two similar approaches used to discover the number and types of trajectories in a study population. Class membership may determine the shape and level of recovery, which may be predicted by individual characteristics. In this talk, we present some insights to using these models to successfully identify the number of classes of trajectories, membership of trajectory classes, and the functional form of the trajectory. We will identify methods for deciding class enumeration, indices for assessing fit quality, and, importantly, the importance of proper model specification. Real life and simulated examples will be shown to compare and contrast differences between GMM and LCTA results. Part of a symposium sponsored by Epidemiology of Aging Interest Group.

A Comparison of Label Switching Algorithms in the Context of Growth Mixture Models

2020 ◽  
pp. 001316442097061
Author(s):  
Kristina R. Cassiday ◽  
Youngmi Cho ◽  
Jeffrey R. Harring
Keyword(s):  
Mixture Models ◽  
Latent Class ◽  
A Priori ◽  
Parameter Estimates ◽  
Label Switching ◽  
Growth Mixture Model ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Class Separation ◽  
Post Hoc

Simulation studies involving mixture models inevitably aggregate parameter estimates and other output across numerous replications. A primary issue that arises in these methodological investigations is label switching. The current study compares several label switching corrections that are commonly used when dealing with mixture models. A growth mixture model is used in this simulation study, and the design crosses three manipulated variables—number of latent classes, latent class probabilities, and class separation, yielding a total of 18 conditions. Within each of these conditions, the accuracy of a priori identifiability constraints, a priori training of the algorithm, and four post hoc algorithms developed by Tueller et al.; Cho; Stephens; and Rodriguez and Walker are tested to determine their classification accuracy. Findings reveal that, of all a priori methods, training of the algorithm leads to the most accurate classification under all conditions. In a case where an a priori algorithm is not selected, Rodriguez and Walker’s algorithm is an excellent choice if interested specifically in aggregating class output without consideration as to whether the classes are accurately ordered. Using any of the post hoc algorithms tested yields improvement over baseline accuracy and is most effective under two-class models when class separation is high. This study found that if the class constraint algorithm was used a priori, it should be combined with a post hoc algorithm for accurate classification.

81 Identifying and predicting novel classes of long-term disease trajectories for patients with juvenile dermatomyositis using growth mixture models

2019 ◽  
Author(s):  
Claire Deakin ◽  
Charalampia Papadopoulou ◽  
Muthana Al Obaidi ◽  
Clarissa Pilkington ◽  
Lucy Wedderburn ◽  
...  
Keyword(s):  
Mixture Models ◽  
Juvenile Dermatomyositis ◽  
Growth Mixture Models ◽  
Growth Mixture
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