1,540 research outputs found
Estimating Knots and Their Association in Parallel Bilinear Spline Growth Curve Models in the Framework of Individual Measurement Occasions
Latent growth curve models with spline functions are flexible and accessible
statistical tools for investigating nonlinear change patterns that exhibit
distinct phases of development in manifested variables. Among such models, the
bilinear spline growth model (BLSGM) is the most straightforward and intuitive
but useful. An existing study has demonstrated that the BLSGM allows the knot
(or change-point), at which two linear segments join together, to be an
additional growth factor other than the intercept and slopes so that
researchers can estimate the knot and its variability in the framework of
individual measurement occasions. However, developmental processes usually
unfold in a joint development where two or more outcomes and their change
patterns are correlated over time. As an extension of the existing BLSGM with
an unknown knot, this study considers a parallel BLSGM (PBLSGM) for
investigating multiple nonlinear growth processes and estimating the knot with
its variability of each process as well as the knot-knot association in the
framework of individual measurement occasions. We present the proposed model by
simulation studies and a real-world data analysis. Our simulation studies
demonstrate that the proposed PBLSGM generally estimate the parameters of
interest unbiasedly, precisely and exhibit appropriate confidence interval
coverage. An empirical example using longitudinal reading scores, mathematics
scores, and science scores shows that the model can estimate the knot with its
variance for each growth curve and the covariance between two knots. We also
provide the corresponding code for the proposed model.Comment: \c{opyright} 2020, American Psychological Association. This paper is
not the copy of record and may not exactly replicate the final, authoritative
version of the article. Please do not copy or cite without authors'
permission. The final article will be available, upon publication, via its
DOI: 10.1037/met000030
Breastfeeding after Gestational Diabetes: Does Perceived Benefits Mediate the Relationship?
Introduction. Breastfeeding is recognized as one of the best ways to decrease infant mortality and morbidity. However, women with gestational diabetes mellitus (GDM) may have breastfeeding barriers due to the increased risk of neonatal and pregnancy complications. While the prevalence of GDM is increasing worldwide, it is important to understand the full implications of GDM on breastfeeding outcomes.The current study aims to investigate the (1) direct effect of GDM on breastfeeding duration and (2) indirect effect of GDM on breastfeeding duration through perceived benefits of breastfeeding. Methods. Prospective cohort data from the Infant Feeding and Practices Study II was analyzed (=4,902). Structural equation modeling estimated direct and indirect effects. Results. Perceived benefits of breastfeeding directly influenced breastfeeding duration ( = 0.392, ≤ 0.001). GDM was not directly associated with breastfeeding duration or perceived benefits of breastfeeding. Similarly, GDM did not have an indirect effect on breastfeeding duration through perceived benefits of breastfeeding. Conclusions. Perceived benefits of breastfeeding are an important factor associated with breastfeeding duration. Maternal and child health care professionals should enhance breastfeeding education efforts
Hybridizing two-step growth mixture model and exploratory factor analysis to examine heterogeneity in nonlinear trajectories
Empirical researchers are usually interested in investigating the impacts of
baseline covariates have when uncovering sample heterogeneity and separating
samples into more homogeneous groups. However, a considerable number of studies
in the structural equation modeling (SEM) framework usually start with vague
hypotheses in terms of heterogeneity and possible reasons. It suggests that (1)
the determination and specification of a proper model with covariates is not
straightforward, and (2) the exploration process may be computational intensive
given that a model in the SEM framework is usually complicated and the pool of
candidate covariates is usually huge in the psychological and educational
domain where the SEM framework is widely employed. Following
\citet{Bakk2017two}, this article presents a two-step growth mixture model
(GMM) that examines the relationship between latent classes of nonlinear
trajectories and baseline characteristics. Our simulation studies demonstrate
that the proposed model is capable of clustering the nonlinear change patterns,
and estimating the parameters of interest unbiasedly, precisely, as well as
exhibiting appropriate confidence interval coverage. Considering the pool of
candidate covariates is usually huge and highly correlated, this study also
proposes implementing exploratory factor analysis (EFA) to reduce the dimension
of covariate space. We illustrate how to use the hybrid method, the two-step
GMM and EFA, to efficiently explore the heterogeneity of nonlinear trajectories
of longitudinal mathematics achievement data.Comment: Draft version 1.6, 08/08/2020. This paper has not been peer reviewed.
Please do not copy or cite without author's permissio
Applying Growth Mixture Model to Assess Heterogeneity in Joint Development with Nonlinear Trajectories in the Framework of Individual Measurement Occasions
Researchers continue to be interested in exploring the effects that
covariates have on the heterogeneity in trajectories. The inclusion of
covariates associated with latent classes allows for a more clear understanding
of individual differences and a more meaningful interpretation of latent class
membership. Many theoretical and empirical studies have focused on
investigating heterogeneity in change patterns of a univariate repeated outcome
and examining the effects on baseline covariates that inform the cluster
formation. However, developmental processes rarely unfold in isolation;
therefore, empirical researchers often desire to examine two or more outcomes
over time, hoping to understand their joint development where these outcomes
and their change patterns are correlated. This study examines the heterogeneity
in parallel nonlinear trajectories and identifies baseline characteristics as
predictors of latent classes. Our simulation studies show that the proposed
model can tell the clusters of parallel trajectories apart and provide unbiased
and accurate point estimates with target coverage probabilities for the
parameters of interest in general. We illustrate how to apply the model to
investigate the heterogeneity in the joint development of reading and
mathematics ability from Grade K to 5. In this real-world example, we also
demonstrate how to select covariates that contribute the most to the latent
classes and transform candidate covariates from a large set into a more
manageable set with retaining the meaningful properties of the original set in
the structural equation modeling framework.Comment: Draft version 1.3, 06/01/2021. This paper has not been peer reviewed.
Please do not copy or cite without author's permissio
Extending Mixture of Experts Model to Investigate Heterogeneity of Trajectories: When, Where and How to Add Which Covariates
Researchers are usually interested in examining the impact of covariates when
separating heterogeneous samples into latent classes that are more homogeneous.
The majority of theoretical and empirical studies with such aims have focused
on identifying covariates as predictors of class membership in the structural
equation modeling framework. In other words, the covariates only indirectly
affect the sample heterogeneity. However, the covariates' influence on
between-individual differences can also be direct. This article presents a
mixture model that investigates covariates to explain within-cluster and
between-cluster heterogeneity simultaneously, known as a mixture-of-experts
(MoE) model. This study aims to extend the MoE framework to investigate
heterogeneity in nonlinear trajectories: to identify latent classes, covariates
as predictors to clusters, and covariates that explain within-cluster
differences in change patterns over time. Our simulation studies demonstrate
that the proposed model generally estimates the parameters unbiasedly,
precisely and exhibits appropriate empirical coverage for a nominal 95%
confidence interval. This study also proposes implementing structural equation
model forests to shrink the covariate space of the proposed mixture model. We
illustrate how to select covariates and construct the proposed model with
longitudinal mathematics achievement data. Additionally, we demonstrate that
the proposed mixture model can be further extended in the structural equation
modeling framework by allowing the covariates that have direct effects to be
time-varying.Comment: Draft version 1.7, 06/01/2021. This paper has not been peer reviewed.
Please do not copy or cite without author's permissio
Cascade diagrams for depicting complex interventions in randomised trials
Clarity about how trial interventions are delivered is important for researchers and those who might want to use their results. A new graphical representation aims to help make complex interventions clearer. Many medical interventions—particularly non-pharmacological ones—are complex, consisting of multiple interacting components targeted at different organisational levels. Published descriptions of complex interventions often do not contain enough detail to enable their replication. Reports of behaviour change interventions should include descriptions of setting, mode, intensity, and duration, and characteristics of the participants. Graphical methods, such as that showing the relative timing of assessments and intervention components, may improve clarity of reporting. However, these approaches do not reveal the connections between the different “actors” in a complex intervention.8 Different audiences may want different things from a description of an intervention, but visualising relationships between actors can clarify crucial features such as the fidelity with which the intervention is passed down a chain of actors and possible routes of contamination between treatment arms. Here we describe a new graphical approach—the cascade diagram—that highlights these potential problems
Identifying Attrition Phases in Survey Data: Applicability and Assessment Study
Background: Although Web-based questionnaires are an efficient, increasingly popular mode of data collection, their utility is often challenged by high participant dropout. Researchers can gain insight into potential causes of high participant dropout by analyzing the dropout patterns.
Objective: This study proposed the application of and assessed the use of user-specified and existing hypothesis testing methods in a novel setting—survey dropout data—to identify phases of higher or lower survey dropout.
Methods: First, we proposed the application of user-specified thresholds to identify abrupt differences in the dropout rate. Second, we proposed the application of 2 existing hypothesis testing methods to detect significant differences in participant dropout. We assessed these methods through a simulation study and through application to a case study, featuring a questionnaire addressing decision-making surrounding cancer screening.
Results: The user-specified method set to a low threshold performed best at accurately detecting phases of high attrition in both the simulation study and test case application, although all proposed methods were too sensitive.
Conclusions: The user-specified method set to a low threshold correctly identified the attrition phases. Hypothesis testing methods, although sensitive at times, were unable to accurately identify the attrition phases. These results strengthen the case for further development of and research surrounding the science of attrition
Obtaining interpretable parameters from reparameterizing longitudinal models: transformation matrices between growth factors in two parameter-spaces
The linear spline growth model (LSGM), which approximates complex patterns
using at least two linear segments, is a popular tool for examining nonlinear
change patterns. Among such models, the linear-linear piecewise change pattern
is the most straightforward one. An earlier study has proved that other than
the intercept and slopes, the knot (or change-point), at which two linear
segments join together, can be estimated as a growth factor in a
reparameterized longitudinal model in the latent growth curve modeling
framework. However, the reparameterized coefficients were no longer directly
related to the underlying developmental process and therefore lacked
meaningful, substantive interpretation, although they were simple functions of
the original parameters. This study proposes transformation matrices between
parameters in the original and reparameterized models so that the interpretable
coefficients directly related to the underlying change pattern can be derived
from reparameterized ones. Additionally, the study extends the existing
linear-linear piecewise model to allow for individual measurement occasions,
and investigates predictors for the individual-differences in change patterns.
We present the proposed methods with simulation studies and a real-world data
analysis. Our simulation studies demonstrate that the proposed method can
generally provide an unbiased and consistent estimation of model parameters of
interest and confidence intervals with satisfactory coverage probabilities. An
empirical example using longitudinal mathematics achievement scores shows that
the model can estimate the growth factor coefficients and path coefficients
directly related to the underlying developmental process, thereby providing
meaningful interpretation. For easier implementation, we also provide the
corresponding code for the proposed models.Comment: Draft version 1.6, 07/28/2020. This paper has not been peer reviewed.
Please do not copy or cite without author's permissio
Sex and sexual orientation disparities in adverse childhood experiences and early age at sexual debut in the United States: Results from a nationally representative sample
Adverse childhood experiences (ACEs) have been linked to early sexual debut, which has been found to be associated with multiple adverse health outcomes. Sexual minorities and men tend to have earlier sexual debut compared to heterosexual populations and women, respectively. However, studies examining the association between ACEs and early sexual debut among men and sexual minorities are lacking. The aim of this study was to examine the sex and sexual orientation disparities in the association between ACEs and age at sexual debut. Data were obtained from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Logistic and linear regression model were used to obtain crude and adjusted estimates and 95% confidence intervals adjusting for age, race/ethnicity, income, education, insurance and marital status for the association between ACEs (neglect, physical/psychological abuse, sexual abuse, parental violence, and parental incarceration and psychopathology) and early sexual debut. Analyses were stratified by sex and sexual orientation. Larger effect estimates depicting the association between ACEs and sexual debut were seen for women compared to men, and among sexual minorities, particularly among men who have sex with men (MSM) and women who have sex with women (WSW), compared to heterosexuals. Sexual health education programs with a focus on delaying sexual debut among children and adolescents should also consider addressing ACEs, such as neglect, physical, psychological and sexual abuse, witnessing parental violence, and parental incarceration and psychopathology. Public health practitioners, researchers and sexual health education curriculum coordinators should consider these differences by sex and sexual orientation when designing these programs
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