Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models

In this paper, an ordinal multilevel latent Markov model based on separate random effects is proposed. In detail, two distinct second-level discrete effects are considered in the model, one affecting the initial probability vector and the other affecting the transition probability matrix of the first-level ordinal latent Markov process. To model these separate effects, we consider a bi-dimensional mixture specification that allows to avoid unverifiable assumptions on the random effect distribution and to derive a two-way clustering of second-level units. Starting from a general model where the two random effects are dependent, we also obtain the independence model as a special case. The proposal is applied to data on the physical health status of a sample of elderly residents grouped into nursing homes. A simulation study assessing the performance of the proposal is also included.

A 16-year prospective cohort study to evaluate effects of long-term fluctuations in obesity indices of prediabetics on the incidence of future diabetes

This study aimed to evaluate the patterns of changes in obesity indices over time in prediabetic subjects and to classify these subjects as either having a low, moderate, and high risk for developing diabetes in the future. This study was conducted among 1228 prediabetics. The patterns of changes in obesity indices based on three measurements including first, mean values during the follow-up period, and last visit from these indices were evaluated by using the latent Markov model (LMM). The mean (standard deviation) age of subjects was 44.0 (6.8) years and 73.6% of them were female. LMM identified three latent states of subjects in terms of change in all anthropometric indices: a low, moderate, and high tendency to progress diabetes with the state sizes (29%, 45%, and 26%), respectively. LMM showed that the probability of transitioning from a low to a moderate tendency to progress diabetes was higher than the other transition probabilities. Based on a long-term evaluation of patterns of changes in obesity indices, our results reemphasized the values of all five obesity indices in clinical settings for identifying high-risk prediabetic subjects for developing diabetes in future and the need for more effective obesity prevention strategies.

Do generous unemployment benefits facilitate transition into more-secure labour market positions? Insights from a multilevel latent Markov model

A large body of research suggests that generous welfare provisions for jobseekers create a disincentive to work. Other scholars argue that generous benefits can reduce unemployment by serving as a job-search subsidy. One caveat in this literature is that, when testing the two hypotheses, many scholars conceive of labour markets as homogeneous entities or they theoretically assume a certain insider/outsider divide. In this article, we claim that the employment effect of generous benefits varies between labour market segments. Analysing EU-SILC panel data of 27 European countries, we find that more-generous unemployment cash benefits enhance the transition from unemployment into more-secure work while discouraging transition into less-secure work in terms of temporal, economic and organisational security. Contrary to existing research, welfare generosity is measured by aggregated information on individual benefit receipt. Labour market segments are identified by latent class analysis and transitions between segments are estimated by Multilevel Latent Markov Models.

Patterns of changes in serum lipid profile in prediabetic patients: Results from a 16-year prospective cohort study

Background Lipids abnormality pervasively is associated with the risk of Type 2 diabetes mellitus. To the best of our knowledge, there is no study that examined the longitudinal changes in wide range of serum lipid profile in prediabetic subjects in association with the risk of Type 2 diabetes mellitus in future. This study aimed to identify the patterns of changes in lipids profile over time in prediabetic patients and classify these subjects in order to highlight the high risk people for future diabetes risk. Methods This prospective 16-year (2003–2019) cohort study was conducted among 1228 prediabetic subjects. The study subjects followed over time and changes in their lipid profile include Triglycerides, Cholesterol, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol was evaluated. Latent Markov model was used for data analysis. Results Mean (standard deviation) age of subjects was 44.00 (6.86) years and 73.6% were female. Latent Markov model identified two latent states of subjects in terms of change in lipid profile: low tendency to progress diabetes/ high tendency to progress diabetes (74%/ 26%). Latent Markov model showed that the transition probability from “low tendency to progress diabetic” state to “high tendency to progress diabetic” state was lower than the transition probability from “high tendency to progress diabetic” state to “low tendency to progress diabetic” state. Conclusion In conclusion, abnormality of serum lipid profile remains a significant and growing problem in prediabetic subjects as high risk population. The reduction in the problem burden will require changes at the policy level as well as at the personal level.

Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis: A Three-Step Approach

Invariance of the measurement model (MM) between subjects and within subjects over time is a prerequisite for drawing valid inferences when studying dynamics of psychological factors in intensive longitudinal data. To conveniently evaluate this invariance, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with mixture factor analysis: The Markov model captures changes in MMs over time by clustering subjects’ observations into a few states and state-specific factor analyses reveal what the MMs look like. However, to estimate the model, the authors employed a full information maximum likelihood (FIML) approach that is counterintuitive for applied researchers and entails cumbersome model selection procedures in the presence of many covariates. In this paper, we simplify the complex LMFA estimation and facilitate the exploration of covariate effects on state memberships by splitting the estimation in three intuitive steps: (1) obtain states with mixture factor analysis while treating repeated measures as independent, (2) assign observations to the states, and (3) use these states in a discrete- or continuous-time latent Markov model taking into account classification errors. A real data example demonstrates the empirical value.