Group-Based Modeling of Development

Nagin, Daniel S.

doi:10.4159/9780674041318

Cited by 3,080 publications

(4,590 citation statements)

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“…This study used growth mixture modeling (Muthen, 2002;Nagin 2005) to examine attendance trajectories among MA primary female caregivers enrolled in a universal preventive intervention. Latent methodologies such as GMM that estimate unobserved heterogeneity as multiple, unique trajectories can inform expectations about differential intervention responses and provide a better representation of the target population's adoption of a program.…”

Section: Discussionmentioning

confidence: 99%

“…We used MPLUS 7.11 to conduct GMM to estimate attendance trajectories (Muthen & Muthen, 2013;Nagin, 2005). Like traditional growth modeling, GMM estimates latent growth factors (i.e., intercept and slope) to model trajectories of repeated observed outcomes; but GMM assumes unobserved heterogeneity in the population and models multiple trajectories with unique intercepts and slopes to capture this heterogeneity.…”

Section: Data Analytic Methodsmentioning

confidence: 99%

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Participation Patterns Among Mexican–American Parents Enrolled in a Universal Intervention and Their Association with Child Externalizing Outcomes

Mauricio

Tein

Gonzales

et al. 2014

American J of Comm Psychol

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This study used growth mixture modeling to examine attendance trajectories among 292Mexican American primary female caregivers enrolled in a universal preventive intervention and the effects of health beliefs, participation intentions, cultural influences, and intervention group cohesion on trajectory group membership as well as trajectory group differences on a distal higher externalizing children with lower GPAs. Caregivers in the two persistent attender groups reported strong group cohesion and providers rated these caregivers as having strong participation intentions. Children of caregivers in the LRPA group had the lowest T2 child externalizing. Children of caregivers in the MPT group had lower T2 externalizing than did those of the ET group, suggesting partial intervention dosage can benefit families. Despite high levels of attendance, children of caregivers in the HRPA had the highest T2 externalizing, suggesting this high-risk group needed either more intensive services or a longer period for parents to implement program skills to evidence change in child externalizing.

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Section: Discussionmentioning

confidence: 99%

Section: Data Analytic Methodsmentioning

confidence: 99%

Participation Patterns Among Mexican–American Parents Enrolled in a Universal Intervention and Their Association with Child Externalizing Outcomes

Mauricio

Tein

Gonzales

et al. 2014

American J of Comm Psychol

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“…These growth models are quite flexible, incorporating linear or nonlinear growth patterns, interactions with baseline variables, intervention changes that affect the variance or covariance as well as the mean pattern of growth, and varying intervention impact across different patterns of growth, rather than an effect that is homogeneous across the entire population. These methods also have flexible ways of dealing with non-normal distributions, including the use of Two-Part (Olsen and Schafer, 2001), and related censoring models (Nagin, 2005) for drug use and other data where zero use is its own special category, as well as for binary, ordinal, and time-to-event data Muthén, 1998-2007). Elsewhere, we have described these different types of growth models and shown their use on the First BPP trial impact analyses of the GBG ; therefore in this paper we illustrate the range of the use of these models in RFTs.…”

Section: Analytical Strategies For Examining Variation In Interventiomentioning

confidence: 99%

“…Growth mixture models were first introduced to understand variation in growth patterns that occurred in a population (Muthén and Shedden, 1999;Nagin, 2005;Nagin and Tremblay, 2001;Pearson et al, 1994;Verbeke and Lesaffre, 1996). The use of these methods to detect variation in intervention impact across subgroups of individuals was applied a few years later (Muthén et al, 2002).…”

Section: Growth Mixture Modelsmentioning

confidence: 99%

“…(2c). If a model specifies that there is no variance within these classes (Nagin, 2005;Nagin and Land, 1993), one of the consequences of this formulation is that individuals in this class have measures that are independent across time. In data such as the First BPP trial, this lack of autocorrelation across time is strongly rejected by the data.…”

Section: Growth Mixture Modelsmentioning

confidence: 99%

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Methods for testing theory and evaluating impact in randomized field trials: Intent-to-treat analyses for integrating the perspectives of person, place, and time

Brown¹,

Wang²,

Kellam³

et al. 2008

Drug and Alcohol Dependence

152

139

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Randomized field trials provide unique opportunities to examine the effectiveness of an intervention in real world settings and to test and extend both theory of etiology and theory of intervention. These trials are designed not only to test for overall intervention impact but also to examine how impact varies as a function of individual level characteristics, context, and across time. Examination of such variation in impact requires analytical methods that take into account the trial's multiple nested structure and the evolving changes in outcomes over time. The models that we describe here merge multilevel modeling with growth modeling, allowing for variation in impact to be represented through discrete mixtures-growth mixture models-and nonparametric smooth functions-generalized additive mixed models. These methods are part of an emerging class of multilevel growth mixture models, and we illustrate these with models that examine overall impact and variation in impact. In this paper, we define intent-to-treat analyses in group-randomized multilevel field trials and discuss appropriate ways to identify, examine, and test for variation in impact without inflating the Type I error rate. We describe how to make causal inferences more robust to misspecification of covariates * Corresponding author. Tel.: +1 813 974 6672. E-mail address: hbrown@health.usf.edu (C.H. Brown). Conflict of InterestAuthor Muthén is a co-developer of Mplus, which is discussed in this paper. There are no conflicts of interest. in such analyses and how to summarize and present these interactive intervention effects clearly. Practical strategies for reducing model complexity, checking model fit, and handling missing data are discussed using six randomized field trials to show how these methods may be used across trials randomized at different levels. NIH Public Access

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Girls' Hyperactivity and Physical Aggression During Childhood and Adjustment Problems in Early Adulthood

Fontaine¹,

Carbonneau²,

Barker³

et al. 2008

Arch Gen Psychiatry

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Group-Based Modeling of Development

Cited by 3,080 publications

References 0 publications

Participation Patterns Among Mexican–American Parents Enrolled in a Universal Intervention and Their Association with Child Externalizing Outcomes

Participation Patterns Among Mexican–American Parents Enrolled in a Universal Intervention and Their Association with Child Externalizing Outcomes

Methods for testing theory and evaluating impact in randomized field trials: Intent-to-treat analyses for integrating the perspectives of person, place, and time

Girls' Hyperactivity and Physical Aggression During Childhood and Adjustment Problems in Early Adulthood

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