Hwan Jeong

INTRODUCTION: Accumulating evidence suggests that an imbalance between pro-angiogenic (i.e., vascular endothelial growth factor (VEGF) and placental growth factor (PlGF)) and anti-angiogenic factors (i.e., soluble VEGF receptor-1 (sVEGFR-1, also referred to as sFlt1)) is involved in the pathophysiology of preeclampsia (PE). Endoglin is a protein that regulates the pro-angiogenic effects of transforming growth factor beta, and its soluble form has recently been implicated in the pathophysiology o

The authors examined the associations between placental vascular findings and preterm delivery in 1,053 subcohort women (239 preterm, 814 term) from a Michigan pregnancy cohort study (1998-2004). Twenty-nine placental vascular variables from microscopic examinations were grouped into 5 constructs: 3 maternal constructs-obstructive lesions (MV-O), bleeding/vessel integrity (MV-I), and lack of physiologic conversion of maternal spiral arteries (MV-D)--and 2 fetal constructs--obstructive lesions (F

BACKGROUND: Measurement invariance of Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) post-traumatic stress disorder (PTSD) criterion symptoms was tested by gender and trauma type, assaultive and non-assaultive. METHOD: Analysis was conducted using latent class analysis (LCA), based on findings that the three-class LCA model from Breslau et al. (Archives of General Psychiatry 2005, 62, 1343-1351) fits the data across the four groups best. The classes represent three l

We investigate the impact of pubertal development, age, and its interaction on female substance use behaviour. An extended latent transition model with two latent variables is used to reflect the dependency of adolescent substance use on pubertal development and age. A sample of females in grades 7-12 is analysed using maximum-likelihood estimation. Analyses indicate that experiencing puberty is related to increased substance use for all age groups. Among females aged 12-15, those who have exper

Summary Analysing the use of marijuana is challenging in part because there is no widely accepted single measure of individual use. Similarly, there is no single response variable that effectively captures attitudes toward its social and moral acceptability. One approach is to view the joint distribution of multiple use and attitude indicators as a mixture of latent classes. Pooling items from the annual ‘Monitoring the future’ surveys of American high school seniors from 1977 to 2001, we find t

Likelihood functions from finite mixture models have many unusual features. Maximum likelihood (ML) estimates may behave poorly over repeated samples, and the abnormal shape of the likelihood often makes it difficult to assess the uncertainty in parameter estimates. Bayesian inference via Markov chain Monte Carlo (MCMC) can be a useful alternative to ML, but the component labels may switch during the MCMC run, making the output difficult to interpret. Two basic methods for handling the label-swi

Group similarities and differences may manifest themselves in a variety of ways in multiple-group latent class analysis (LCA). Sometimes, measurement models are identical across groups in LCA. In other situations, the measurement models may differ, suggesting that the latent structure itself is different between groups. Tests of measurement invariance shed light on this distinction. We created an R package glca that implements procedures for exploring differences in latent class structure betwee

Parameters for latent transition analysis (LTA) are easily estimated by maximum likelihood (ML) or Bayesian method via Markov chain Monte Carlo (MCMC). However, unusual features in the likelihood can cause difficulties in ML and Bayesian inference and estimation, especially with small samples. In this study we explore several problems in drawing inference for LTA in the context of a simulation study and a substance use example. We argue that when conventional ML and Bayesian estimates behave err

Earlier age of drinking is a well-known predictor for a variety of adverse public health consequences in the United States and worldwide. In longitudinal research on early-onset drinkers, a great deal of attention has been paid to the identification of subgroups of individuals who follow similar sequential patterns of drinking behaviours. However, research on the sequential development of drinking behaviour can be challenging in part because it may not be possible to directly observe the particu

Latent transition models increasingly include covariates that predict prevalence of latent classes at a given time or transition rates among classes over time. In many situations, the covariate of interest may be latent. This paper describes an approach for handling both manifest and latent covariates in a latent transition model. A Bayesian approach via Markov chain Monte Carlo (MCMC) is employed in order to achieve more robust estimates. A case example illustrating the model is provided using

This paper proposes a new type of latent class analysis, joint latent class analysis (JLCA), which provides a set of principles for the systematic identification of the subsets of joint patterns for multiple discrete latent variables. Inferences about the parameters are obtained by a hybrid method of EM and Newton-Raphson algorithms. We apply JLCA in an investigation of adolescent violent behavior and drug-using behaviors. The data are from 4,957 male high-school students who participated in the

This article presents a multiple-group latent class-profile analysis (LCPA) by taking a Bayesian approach in which a Markov chain Monte Carlo simulation is employed to achieve more robust estimates for latent growth patterns. This article describes and addresses a label-switching problem that involves the LCPA likelihood function, which has multiple equivalent modes because it is invariant to permutations of class and profile labels. Our solution involves a dynamic data-dependent prior that can