scholarly journals Exploring the change patterns in retirees’ psychological health using growth mixture modeling

2015 ◽  
Vol 28 (2) ◽  
pp. 99-126
Author(s):  
Songhwa Doo ◽  
JaeYoon Chang
Keyword(s):  
Psychological Health ◽  
Role Theory ◽  
Unobserved Heterogeneity ◽  
Economic Status ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Developmental Trajectory ◽  
Growth Mixture ◽  
Factors Influencing ◽  
Different Types

Using growth mixture modeling which tracks unobserved heterogeneity in population, the current study explored the change patterns of retirees’ psychological health and investigated factors influencing each developmental trajectory. Specifically, based on role theory, continuity theory, and resources theory, it was hypothesized that retirees would display three different types of psychological patterns: the maintaining, enhancing, and declining pattern. In order to test the above expectation, I adapted panel data of the Korean Retirement and Income Study including a total of 436 retirees who participated in survey every other year since 2005 to 2011. Results revealed that there are two distinct change patterns of retirees’ psychological health: the enhancing pattern and the declining pattern. Following was logistic regression analysis which investigated factors influencing each pattern. As a result, it was found that those with higher education and pre-retire satisfaction on health and economic status were more likely to display the enhancing pattern. On the other hand, those who retired late and retired due to health problems were more likely to display the declining pattern.

scholarly journals General Approaches to Analysis of Course: Applying Growth Mixture Modeling to Randomized Trials of Depression Medication

2011 ◽  
Author(s):  
Bengt Muthé N ◽  
Hendricks C. Brown
Keyword(s):  
Treatment Effects ◽  
Randomized Trials ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Mixed Effects ◽  
Class Membership ◽  
Growth Mixture ◽  
Mixed Effects Modeling ◽  
Different Types ◽  
And Cluster Analysis

This chapter discusses the assessment of treatment effects in longitudinal randomized trials using growth mixture modeling (GMM) (Muthén & Shedden, 1999; Muthén & Muthén, 2000; Muthén et al., 2002; Muthén & Asparouhov, 2009). GMM is a generalization of conventional repeated measurement mixed-effects (multilevel) modeling. It captures unobserved subject heterogeneity in trajectories not only by random effects but also by latent classes corresponding to qualitatively different types of trajectories. It can be seen as a combination of conventional mixed-effects modeling and cluster analysis, also allowing prediction of class membership and estimation of each individual’s most likely class membership. GMM has particularly strong potential for analyses of randomized trials because it responds to the need to investigate for whom a treatment is effective by allowing for different treatment effects in different trajectory classes. The chapter is motivated by a University of California, Los Angeles study of depression medication (Leuchter, Cook, Witte, Morgan, & Abrams, 2002). Data on 94 subjects are drawn from a combination of three studies carried out with the same design, using three different types of medications: fluoxetine (n = 14), venlafaxine IR (n = 17), and venlafaxine XR (n = 18). Subjects were measured at baseline and again after a 1-week placebo lead-in phase. In the subsequent double-blind phase of the study, the subjects were randomized into medication (n = 49) and placebo (n = 45) groups. After randomization, subjects were measured at nine occasions: at 48 hours and at weeks 1–8. The current analyses consider the Hamilton Depression Rating Scale. Several predictors of course of the Hamilton scale trajectory are available, including gender, treatment history, and a baseline measure of central cordance hypothesized to influence tendency to respond to treatment. The results of studies of this kind are often characterized in terms of an end point analysis where the outcome at the end of the study, here at 8 weeks, is considered for the placebo group and for the medication group.

Die Identifikation früher Veränderungsmuster in der ambulanten Psychotherapie

2007 ◽  
Vol 36 (2) ◽  
pp. 93-104 ◽  
Author(s):  
Wolfgang Lutz ◽  
Niklaus Stulz ◽  
David W. Smart ◽  
Michael J. Lambert
Keyword(s):  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Growth Mixture

Zusammenfassung. Theoretischer Hintergrund: Im Rahmen einer patientenorientierten Psychotherapieforschung werden Patientenausgangsmerkmale und Veränderungsmuster in einer frühen Therapiephase genutzt, um Behandlungsergebnisse und Behandlungsdauer vorherzusagen. Fragestellung: Lassen sich in frühen Therapiephasen verschiedene Muster der Veränderung (Verlaufscluster) identifizieren und durch Patientencharakteristika vorhersagen? Erlauben diese Verlaufscluster eine Vorhersage bezüglich Therapieergebnis und -dauer? Methode: Anhand des Growth Mixture Modeling Ansatzes wurden in einer Stichprobe von N = 2206 ambulanten Patienten einer US-amerikanischen Psychotherapieambulanz verschiedene latente Klassen des frühen Therapieverlaufs ermittelt und unter Berücksichtigung unterschiedlicher Patientenausgangscharakteristika als Prädiktoren der frühen Veränderungen mit dem Therapieergebnis und der Therapiedauer in Beziehung gesetzt. Ergebnisse: Für leicht, mittelschwer und schwer beeinträchtigte Patienten konnten je vier unterschiedliche Verlaufscluster mit jeweils spezifischen Prädiktoren identifiziert werden. Die Identifikation der frühen Verlaufsmuster ermöglichte weiterhin eine spezifische Vorhersage für die unterschiedlichen Verlaufscluster bezüglich des Therapieergebnisses und der Therapiedauer. Schlussfolgerungen: Frühe Psychotherapieverlaufsmuster können einen Beitrag zu einer frühzeitigen Identifikation günstiger sowie ungünstiger Therapieverläufe leisten.

A Monte Carlo evaluation of growth mixture modeling

2021 ◽  
pp. 1-14
Author(s):  
Tiffany M. Shader ◽  
Theodore P. Beauchaine
Keyword(s):  
Monte Carlo ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Fit Indices ◽  
Linear Quadratic ◽  
Growth Mixture ◽  
True Number ◽  
Monte Carlo Evaluation ◽  
Wrong Number ◽  
State Of Affairs

Abstract Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1–4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature.

scholarly journals Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups

2009 ◽  
Vol 33 (6) ◽  
pp. 565-576 ◽  
Author(s):  
Nilam Ram ◽  
Kevin J. Grimm
Keyword(s):  
Stress Response ◽  
Growth Curve ◽  
Growth Modeling ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Longitudinal Change ◽  
Growth Curve Model ◽  
Growth Mixture ◽  
Multiple Group ◽  
Curve Model

Growth mixture modeling (GMM) is a method for identifying multiple unobserved sub-populations, describing longitudinal change within each unobserved sub-population, and examining differences in change among unobserved sub-populations. We provide a practical primer that may be useful for researchers beginning to incorporate GMM analysis into their research. We briefly review basic elements of the standard latent basis growth curve model, introduce GMM as an extension of multiple-group growth modeling, and describe a four-step approach to conducting a GMM analysis. Example data from a cortisol stress-response paradigm are used to illustrate the suggested procedures.

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