scholarly journals Bayesian Latent-Class Mixed-Effect Hybrid Models for Dyadic Longitudinal Data with Non-Ignorable Dropouts

Biometrics ◽  
2013 ◽  
Vol 69 (4) ◽  
pp. 914-924 ◽  
Author(s):  
Jaeil Ahn ◽  
Suyu Liu ◽  
Wenyi Wang ◽  
Ying Yuan
Keyword(s):  
Longitudinal Data ◽  
Latent Class ◽  
Hybrid Models ◽  
Mixed Effect

scholarly journals E-bike user groups and substitution effects: evidence from longitudinal travel data in the Netherlands

2021 ◽  
Author(s):  
Mathijs de Haas ◽  
Maarten Kroesen ◽  
Caspar Chorus ◽  
Sascha Hoogendoorn-Lanser ◽  
Serge Hoogendoorn
Keyword(s):  
The Netherlands ◽  
Longitudinal Data ◽  
Latent Class ◽  
Good Alternative ◽  
Substitution Effects ◽  
Or Education ◽  
Cross Sectional ◽  
User Groups ◽  
Using Data ◽  
Mode Of Transport

AbstractIn recent years, the e-bike has become increasingly popular in many European countries. With higher speeds and less effort needed, the e-bike is a promising mode of transport to many, and it is considered a good alternative for certain car trips by policy-makers and planners. A major limitation of many studies that investigate such substitution effects of the e-bike, is their reliance on cross-sectional data which do not allow an assessment of within-person travel mode changes. As a consequence, there is currently no consensus about the e-bike’s potential to replace car trips. Furthermore, there has been little research focusing on heterogeneity among e-bike users. In this respect, it is likely that different groups exist that use the e-bike for different reasons (e.g. leisure vs commute travel), something which will also influence possible substitution patterns. This paper contributes to the literature in two ways: (1) it presents a statistical analysis to assess the extent to which e-bike trips are substituting trips by other travel modes based on longitudinal data; (2) it reveals different user groups among the e-bike population. A Random Intercept Cross-Lagged Panel Model is estimated using five waves of data from the Netherlands Mobility Panel. Furthermore, a Latent Class Analysis is performed using data from the Dutch national travel survey. Results show that, when using longitudinal data, the substitution effects between e-bike and the competing travel modes of car and public transport are not as significant as reported in earlier research. In general, e-bike trips only significantly reduce conventional bicycle trips in the Netherlands, which can be regarded an unwanted effect from a policy-viewpoint. For commuting, the e-bike also substitutes car trips. Furthermore, results show that there are five different user groups with their own distinct behaviour patterns and socio-demographic characteristics. They also show that groups that use the e-bike primarily for commuting or education are growing at a much higher rate than groups that mainly use the e-bike for leisure and shopping purposes.

Bayesian nonparametric latent class model for longitudinal data

2020 ◽  
Vol 29 (11) ◽  
pp. 3381-3395
Author(s):  
Wonmo Koo ◽  
Heeyoung Kim
Keyword(s):  
Longitudinal Data ◽  
Latent Class ◽  
Latent Class Model ◽  
Latent Class Models ◽  
Model Parameters ◽  
Latent Classes ◽  
Bayesian Nonparametric ◽  
Class Model ◽  
Proposed Model ◽  
Class Models

Latent class models have been widely used in longitudinal studies to uncover unobserved heterogeneity in a population and find the characteristics of the latent classes simultaneously using the class allocation probabilities dependent on predictors. However, previous latent class models for longitudinal data suffer from uncertainty in the choice of the number of latent classes. In this study, we propose a Bayesian nonparametric latent class model for longitudinal data, which allows the number of latent classes to be inferred from the data. The proposed model is an infinite mixture model with predictor-dependent class allocation probabilities; an individual longitudinal trajectory is described by the class-specific linear mixed effects model. The model parameters are estimated using Markov chain Monte Carlo methods. The proposed model is validated using a simulated example and a real-data example for characterizing latent classes of estradiol trajectories over the menopausal transition using data from the Study of Women’s Health Across the Nation.

scholarly journals Youth in a pandemic: a longitudinal examination of youth mental health and substance use concerns during COVID-19

BMJ Open ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. e049209
Author(s):  
Lisa D Hawke ◽  
Peter Szatmari ◽  
Kristin Cleverley ◽  
Darren Courtney ◽  
Amy Cheung ◽  
...  
Keyword(s):  
Mental Health ◽  
Substance Use ◽  
Longitudinal Data ◽  
Young Women ◽  
Latent Class ◽  
Youth Mental Health ◽  
Health Concerns ◽  
Response Strategies ◽  
Mood Symptoms ◽  
Mental Health Concerns

ObjectiveThis study analyses longitudinal data to understand how youth mental health and substance use are evolving over the course of the COVID-19 pandemic, which is critical to adjusting mental health response strategies.SettingParticipants were recruited from among existing participants in studies conducted in an urban academic hospital in Ontario, Canada.ParticipantsA total of 619 youth aged 14–28 years participated in the study (62.7% girls/young women; 61.4% Caucasian).MeasuresData on mood, substance use and COVID-19-related worries were collected over four time points, that is, every 2 months beginning in the early stages of the pandemic in April 2020. Latent class analyses were conducted on the longitudinal data to identify distinct groups of youth who have different trajectory profiles of pandemic impact on their mood, substance use and COVID-19-related worries.ResultsFor the majority of participants, mood concerns increased early in the pandemic, declined over Canada’s summer months and subsequently increased in autumn. Among the youth with the highest level of mood symptoms at the beginning of the pandemic, increases in mental health concerns were sustained. Substance use remained relatively stable over the course of the pandemic. COVID-19-related worries, however, followed a trajectory similar to that of mood symptoms. Girls/young women, youth living in urban or suburban areas, in larger households, and with poorer baseline mental and physical health are the most vulnerable to mental health concerns and worries during the pandemic.ConclusionsYouth mental health symptom levels and concerns are evolving over the course of the COVID-19 pandemic, in line with the evolution of the pandemic itself, and longitudinal monitoring is therefore required. It is also essential that we engage directly with youth to cocreate pandemic response strategies and mental health service adaptations to best meet the needs of young people.

Food Insecurity and Mental Health Trajectories During the COVID-19 Pandemic: Longitudinal Evidence

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 408-409
Author(s):  
Dexia Kong ◽  
Peiyi Lu ◽  
Elissa Kozlov ◽  
Mack Shelley
Keyword(s):  
Mental Health ◽  
Depressive Symptoms ◽  
Longitudinal Data ◽  
Health Outcomes ◽  
Food Insecurity ◽  
Repeated Measures ◽  
Mental Health Outcomes ◽  
Mixed Effect ◽  
Health Trajectories ◽  
Over Time

Abstract The extent to which food insecurity impacts changes in mental health outcomes over time in the context of Covid-19 remains unknown. Using longitudinal data from a nationally representative survey, the objectives of the present study were to: (1) assess the prevalence of food insecurity among U.S. adults amid the Covid-19 pandemic; and (2) investigate the relationships between food insecurity statuses and changes in mental health outcomes over time as the pandemic unfolds. Longitudinal data from the Internet-based Understanding Coronavirus in America survey collected bi-weekly between April and December 2020 were used (n=4,068, 15 repeated measures). Adult respondents (aged ≥18) were asked about their food insecurity experiences and stress/anxiety/depressive symptoms. Linear mixed-effect models examined changes in mental health outcomes over time among groups with various food insecurity statuses. Overall prevalence of food insecurity was 8%. Food insecurity was consistently associated with higher levels of stress/anxiety/depressive symptoms (p<0.001). Stress/anxiety/depressive symptoms declined over time among food-secured U.S adults. However, mental health trajectories of respondents with various food insecurity categories, including food insecurity status, persistent food insecurity, and food insecurity of higher severity and longer duration, remained stable or worsened over time. Moreover, the mental health gap between food-secured and food-unsecured participants widened over time. Food insecurity represents a pressing public health problem during the Covid-19 pandemic with substantial mental health implications. Persistent and severe food insecurity may contribute to mental health disparity in the long term. Food insecurity reduction interventions may alleviate the estimated alarming mental health burden as the pandemic unfolds.

scholarly journals Latent Curve Analyses of Longitudinal Twin Data Using a Mixed-Effects Biometric Approach

2006 ◽  
Vol 9 (3) ◽  
pp. 343-359 ◽  
Author(s):  
John J. McArdle
Keyword(s):  
Longitudinal Data ◽  
Goodness Of Fit ◽  
Mixed Effects ◽  
Simultaneous Estimation ◽  
Twin Data ◽  
Mixed Effect ◽  
Mixed Effects Modeling ◽  
Curve Model ◽  
Latent Curve ◽  
Longitudinal Twin Data

AbstractIn a recent article McArdle and Prescott (2005) showed how simultaneous estimation of the bio-metric parameters can be easily programmed using current mixed-effects modeling programs (e.g., SAS PROC MIXED). This article extends these concepts to deal with mixed-effect modeling of longitudinal twin data. The biometric basis of a polynomial growth curve model was used by Vandenberg and Falkner (1965) and this general class of longitudinal models was represented in structural equation form as a latent curve model by McArdle (1986). The new mixed-effects modeling approach presented here makes it easy to analyze longitudinal growth-decline models with biometric components based on standard maximum likelihood estimation and standard indices of goodness-of-fit (i.e., χ2, df, εa). The validity of this approach is first checked by the creation of simulated longitudinal twin data followed by numerical analysis using different computer programs (i.e., Mplus, Mx, MIXED, NLMIXED). The practical utility of this approach is examined through the application of these techniques to real longitudinal data from the Swedish Adoption/Twin Study of Aging (Pedersen et al., 2002). This approach generally allows researchers to explore the genetic and nongenetic basis of the latent status and latent changes in longitudinal scores in the absence of measurement error. These results show the mixed-effects approach easily accounts for complex patterns of incomplete longitudinal or twin pair data. The results also show this approach easily allows a variety of complex latent basis curves, such as the use of age-at-testing instead of wave-of-testing. Natural extensions of this mixed-effects longitudinal approach include more intensive studies of the available data, the analysis of categorical longitudinal data, and mixtures of latent growth-survival/ frailty models.

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