An Introduction to Latent Variable Mixture Modeling (Part 2): Longitudinal Latent Class Growth Analysis and Growth Mixture Models

Berlin, Kristoffer S.; Parra, Gilbert R.; Williams, Natalie A.

doi:10.1093/jpepsy/jst085

Cited by 318 publications

(281 citation statements)

References 26 publications

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“…However, it is known that these models produce comparable trajectories 32 and are collectively called "group-based trajectory models. " [24][25][26][27][28] With these approaches, it is recommended that the best-fitting model be chosen on the basis of the Bayesian Information Criterion (BIC) scores and an examination of 95% confidence intervals. 27 Typically, investigators estimate models with some trajectories (e.g., 1, 2, 3, …, n), select the best fitting model by comparing the BIC associated with various solutions and the average posterior probabilities of group membership, and evaluate whether successive models identify additional distinct groups as indicated by non-overlapping confidence intervals.…”

Section: Resultsmentioning

confidence: 99%

“…[24][25][26][27][28] This technique assumes the presence of and identifies latent groups of individuals who share a particular developmental trajectory of some attribute, thereby allowing a better understanding of the pattern of change in the variable of interest. The technique has been used extensively in criminology and behavioral research and less in medicine and public health research.…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneity in Trajectories of Body Mass Index and Their Associations with Mortality in Old Age: A Literature Review

Murayama

Shaw

2017

JOMES

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This article reviewed studies to investigate the association between trajectories of body mass index (BMI) and mortality among older adults. Investigators conducted a systematic search of published peer-reviewed literature in the PubMed database, and three articles that satisfied the inclusion criteria for the review were identified. All of these studies used group-based trajectory models to identify distinct BMI trajectories. Two studies were derived from the U.S. and used data from the Health and Retirement Study, with up to nine repeated observations. Most of the BMI trajectories in older Americans were increasing and fell primarily within the overweight and obese ranges. The other study was from Japan and used nationwide data, with up to seven repeated observations. BMI trajectories identified in the older Japanese were mostly decreasing and fell primarily within the normal weight range. Although the distribution of BMI trajectories was different between the two nations, the findings from these three studies consistently demonstrated that people with stable overweight trajectories had the lowest all-cause mortality rates in both countries. Beyond this, however, these studies suggested that priorities for weight control in old age should likely differ between Western and non-Western countries. Research regarding BMI trajectories and mortality in old age is very limited at present. Evidence from countries other than the U.S. and Japan is warranted in order to validate current findings and guide the development of local clinical and public health strategies for body weight management aimed at improving the health and survival of older adults.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heterogeneity in Trajectories of Body Mass Index and Their Associations with Mortality in Old Age: A Literature Review

Murayama

Shaw

2017

JOMES

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“…The censored normal model was used, with uncensored cases handled by specifying a minimum and maximum outside the range of the observed data values (33). The number of groups and degree of polynomial in each trajectory group was determined using the Bayesian Information Criterion, which measures improvement in model fit gained by adding additional groups or shape parameters incorporating a penalty for added complexity (13,14,34). Solutions that included small trajectory groups (<5% of the sample) were rejected.…”

Section: :4 277 Clinical Studymentioning

confidence: 99%

“…Temporal changes in variables can be characterised using latent class analysis which identifies distinctive clusters of individual trajectories in a larger sample, with the simplest model of best fit selected statistically (13,14). This group-based modelling (GBM) approach has been used to differentiate groups of patients by time-related changes in key variables in disease contexts such as public health (15), psychiatry (16) and metabolic disorders (17,18), and in the categorisation of changes in HbA 1c in diabetes (19,20) including FDS1 (21).…”

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confidence: 99%

The relationship between estimated glomerular filtration rate trajectory and all-cause mortality in type 2 diabetes: the Fremantle Diabetes Study

Davis

Chubb

Davis

2016

European Journal of Endocrinology

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Objective: To investigate the association between estimated GFR (eGFR) and all-cause mortality, including the contribution of temporal eGFR changes, in well-characterised community-based patients with type 2 diabetes. Design: Longitudinal observational study. Methods: Participants from the Fremantle Diabetes Study Phase 1 were assessed between 1993 and 1996 and followed until end-December 2012. Cox proportional hazards modelling was used to assess the relationship between baseline eGFR category (Stage 1-5) and all-cause death, and between eGFR trajectories assigned by semiparametric group-based modelling (GBM) and all-cause death in patients with five post-baseline annual eGFR measurements. Results: In the full cohort (1296 patients; mean ± s.d. age 64.1 ± 11.3 years, 48.6% males), 738 (56.9%) died during 12.9 ± 6.1 years of follow-up. There was a U-shaped relationship between all-cause death and eGFR category. With Stage 3 (45-59 mL/min/1.73 m 2 ) as reference, the strongest association was for eGFR ≥90 mL/min/1.73 m 2 (hazard ratio (95% CI) 2.01 (1.52-2.66); P < 0.001). GBM identified four linear trajectories ('low', 'medium', 'high', 'high/declining') in 532 patients with serial eGFR measurements. With medium trajectory as reference, eGFR trajectory displaced baseline eGFR category as an independent predictor of death, with low and high/declining trajectories associated with more than double the risk (2.03 (1.30-3.18) and 2.24 (1.31-3.83) respectively, P ≤ 0.003) and associated median reductions in survival of 6.5 and 8.7 years respectively. Conclusion: There is a nonlinear relationship between eGFR and death in type 2 diabetes, which is at least partially explained by a sub-group of patients with an initially high but then rapidly declining eGFR.

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“…During the past decade, researchers have shown growing interest in applying latent growth curve mixture models (LGCMMs; Berlin et al 2014;Leiby et al 2009;Neelon et al 2011;Ram and Grimm 2009), and, more recently, in applying machine learning techniques including SEM Trees (e.g., Hayes et al 2015;Martin 2015;Jacobucci et al 2017). Both LGCMMs and SEM Trees utilize SEM to model changes using latent variables estimated with a smaller number of parameters.…”

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confidence: 99%

The performance of latent growth curve model-based structural equation model trees to uncover population heterogeneity in growth trajectories

2018

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Behavioral researchers have shown growing interest in structural equation model trees (SEM Trees), a new recursive partitioning-based technique for detecting population heterogeneity. In the present research, we conducted a large-scale simulation to investigate the performance of latent growth curve model (LGCM)-based SEM Trees for uncovering between-individual differences in patterns of within-individual change. Simulation results showed that the correct estimation rates of the number of classes are most strongly related to the agreement rate of the covariate with its true latent profile, and the number of true classes also has a serious negative impact on correct estimation rates of the number of classes. SEM Trees is not always sensitive to the influence of model misspecification, and its impact differs according to a complex function of the types of misspecification as well as the statistical properties of the template model. On the whole, LGCM-based SEM Trees is a robust and stable approach under possible model misspecifications.

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An Introduction to Latent Variable Mixture Modeling (Part 2): Longitudinal Latent Class Growth Analysis and Growth Mixture Models

Abstract: Latent variable mixture modeling is a technique that is useful to pediatric psychologists who wish to find groupings of individuals who share similar longitudinal data patterns to determine the extent to which these patterns may relate to variables of interest.

Cited by 318 publications

References 26 publications

Heterogeneity in Trajectories of Body Mass Index and Their Associations with Mortality in Old Age: A Literature Review

Heterogeneity in Trajectories of Body Mass Index and Their Associations with Mortality in Old Age: A Literature Review

The relationship between estimated glomerular filtration rate trajectory and all-cause mortality in type 2 diabetes: the Fremantle Diabetes Study

The performance of latent growth curve model-based structural equation model trees to uncover population heterogeneity in growth trajectories

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