Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Dimensions and determinants of upward mobility : a study based on longitudinal data from Delhi slums

This study based on two primary surveys of the same households in two different years (2007/08 and 2012) assesses the extent of inter-temporal change in income of the individual workers and makes an attempt to identify the factors which explain upward mobility in alternate econometric framework, envisaging endogeneity problem. It also encompasses a host of indicators of wellbeing and constructs the transition matrix to capture the extent of change over time at the household level. The findings are indicative of a rise in the income of workers across a sizeable percentage of households though many of them remained below the poverty line notwithstanding this increase. In fact, there is a wide spread deterioration in the wellbeing index constructed at the household level. Among several determinants of income rise two important policy prescriptions can be elicited. Inadequate education reduces the probability of upward mobility while education above a threshold level raises it. Savings are crucial for upward mobility impinging on the importance of asset creation. Views that entail neighbourhood spill-over effects also received validation. Besides, investment in housing and basic amenities turns out to be crucial for improvement in wellbeing levels.

Recovery from Pediatric Brain Injury: A Preliminary Comparison of Longitudinal Data on Psychological Symptomology and Mental Health Treatment

Objective: To document the course of psychological symptomology, mental health treatment, and unmet psychological needs using caregiver reports in the first 18 months following pediatric brain injury (BI). Method: Participants included 28 children (aged 1-18 years) who were hospitalized at a children's hospital's rehabilitation unit. Caregiver reports of children's psychological symptoms, receipt of mental health treatment, and unmet psychological needs were assessed at one month, six months, 12 months, and 18 months post-BI. Results: Caregivers reported a general increase in psychological symptoms and receipt of mental health treatment over the 18 months following BI; however, there was a substantial gap between the high rate of reported symptoms and low rate of reported treatment. Across all four follow-up time points there were substantial unmet psychological needs (at least 60% of sample). Conclusions: Findings suggest that there are substantial unmet psychological needs among children during the first 18 months after BI. Barriers to mental health treatment for this population need to be addressed.

Individuals and families in transition : understanding change through longitudinal data : papers presented at the Social Science Research Council conference in Annapolis, Maryland, March 16-18, 1988 /

Mode of access: Internet.

A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants

The paper investigates a Bayesian hierarchical model for the analysis of categorical longitudinal data from a large social survey of immigrants to Australia. Data for each subject are observed on three separate occasions, or waves, of the survey. One of the features of the data set is that observations for some variables are missing for at least one wave. A model for the employment status of immigrants is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response and then subsequent terms are introduced to explain wave and subject effects. To estimate the model, we use the Gibbs sampler, which allows missing data for both the response and the explanatory variables to be imputed at each iteration of the algorithm, given some appropriate prior distributions. After accounting for significant covariate effects in the model, results show that the relative probability of remaining unemployed diminished with time following arrival in Australia.

Three-factor models versus time series models:quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data

Signal integration determines cell fate on the cellular level, affects cognitive processes and affective responses on the behavioural level, and is likely to be involved in psychoneurobiological processes underlying mood disorders. Interactions between stimuli may subjected to time effects. Time-dependencies of interactions between stimuli typically lead to complex cell responses and complex responses on the behavioural level. We show that both three-factor models and time series models can be used to uncover such time-dependencies. However, we argue that for short longitudinal data the three factor modelling approach is more suitable. In order to illustrate both approaches, we re-analysed previously published short longitudinal data sets. We found that in human embryonic kidney 293 cells cells the interaction effect in the regulation of extracellular signal-regulated kinase (ERK) 1 signalling activation by insulin and epidermal growth factor is subjected to a time effect and dramatically decays at peak values of ERK activation. In contrast, we found that the interaction effect induced by hypoxia and tumour necrosis factor-alpha for the transcriptional activity of the human cyclo-oxygenase-2 promoter in HEK293 cells is time invariant at least in the first 12-h time window after stimulation. Furthermore, we applied the three-factor model to previously reported animal studies. In these studies, memory storage was found to be subjected to an interaction effect of the beta-adrenoceptor agonist clenbuterol and certain antagonists acting on the alpha-1-adrenoceptor / glucocorticoid-receptor system. Our model-based analysis suggests that only if the antagonist drug is administer in a critical time window, then the interaction effect is relevant.

Multilevel structural equation models for longitudinal data where predictors are measured more frequently than outcomes : an application to the effects of stress on the cognitive function of nurses

Funded by Chief Scientist Office, Scotland. Grant Number: CZH/4/394 Economic and Social Research Council grant as part of the National Centre for Research Methods. Grant Number: RES-576-25-0032

Leaving and Coming Back to Canada : Evidence from Longitudinal Data (Working Paper 32)

The paper exploits the unique strengths of Statistics Canada's Longitudinal Administrative Database ("LAD"), constructed from individuals' tax records, to shed new light on the extent and nature of the emigration of Canadians to other countries and their patterns of return over the period 1982-1999. The empirical evidence begins with some simple graphs of the overall rates of leaving over time, and follows with the presentation of the estimation results of a model that essentially addresses the question: "who moves?" The paper then analyses the rates of return for those observed to leave the country - something for which there is virtually no existing evidence. Simple return rates are reported first, followed by the results of a hazard model of the probability of returning which takes into account individuals' characteristics and the number of years they have already been out of the country. Taken together, these results provide a new empirical basis for discussions of emigration in general, and the brain drain in particular. Of particular interest are the ebb and flow of emigration rates observed over the last two decades, including a perhaps surprising turndown in the most recent years after climbing through the earlier part of the 1990s; the data on the number who return after leaving, the associated patterns by income level, and the increases observed over the last decade.

Estimating parameters in Markov models for longitudinal studies with missing data or surrogate outcomes

The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis. ^ This dissertation research proposes methods to analyze longitudinal data (1) that have categorical outcome with missing observations or (2) that use complete or incomplete surrogate observations to analyze the categorical latent outcome. For (1), different missing mechanisms were considered for empirical studies using methods that include EM algorithm, Monte Carlo EM and a procedure that is not a data augmentation method. For (2), the hidden Markov model with the forward-backward procedure was applied for parameter estimation. This method was also extended to cover the computation of standard errors. The proposed methods were demonstrated by the Schizophrenia example. The relevance of public health, the strength and limitations, and possible future research were also discussed. ^

Longitudinal categorical data analysis: A continuous -time Markov chain approach

In this dissertation, we propose a continuous-time Markov chain model to examine the longitudinal data that have three categories in the outcome variable. The advantage of this model is that it permits a different number of measurements for each subject and the duration between two consecutive time points of measurements can be irregular. Using the maximum likelihood principle, we can estimate the transition probability between two time points. By using the information provided by the independent variables, this model can also estimate the transition probability for each subject. The Monte Carlo simulation method will be used to investigate the goodness of model fitting compared with that obtained from other models. A public health example will be used to demonstrate the application of this method. ^

How can we exploit mobile data to customise learning based on a student’s environmental surroundings?

User-Based intelligent systems are already commonplace in a student’s online digital life. Each time they browse, search, buy, join, comment, play, travel, upload, download, a system collects, analyses and processes data in an effort to customise content and further improve services. This panel session will explore how intelligent systems, particularly those that gather data from mobile devices, can offer new possibilities to assist in the delivery of customised, personal and engaging learning experiences. The value of intelligent systems for education lies in their ability to formulate authentic and complex learner profiles that bring together and systematically integrate a student’s personal world with a formal curriculum framework. As we well know, a mobile device can collect data relating to a student’s interests (gathered from search history, applications and communications), location, surroundings and proximity to others (GPS, Bluetooth). However, what has been less explored is the opportunity for a mobile device to map the movements and activities of a student from moment to moment and over time. This longitudinal data provides a holistic profile of a student, their state and surroundings. Analysing this data may allow us to identify patterns that reveal a student’s learning processes; when and where they work best and for how long. Through revealing a student’s state and surroundings outside of schools hour, this longitudinal data may also highlight opportunities to transform a student’s everyday world into an inventory for learning, punctuating their surroundings with learning recommendations. This would in turn lead to new ways to acknowledge and validate and foster informal learning, making it legitimate within a formal curriculum.

Four years on – are the Gazelles still running? A longitudinal study of firm performance after a period of rapid growth

Gazelles, or very rapidly growing firms, are important because they contribute disproportionately to economic growth. There is a concern that some of these firms pursue growth too aggressively resulting in lower subsequent performance. We investigate the relationship between growth and subsequent profitability for gazelle firms, and how this is moderated by firm strategy. Previous empirical research regarding the growth-profitability relationship for firms in general is rather inconclusive, with only one study specifically investigating gazelle firms. Likewise, there are theoretical arguments both for and against growth leading to profitability that equally apply to gazelle firms. Further, while contingency theory might suggest the relationship depends on the firm’s strategy, earlier studies have not investigated this relationship. We address these questions using longitudinal data (seven years) for a sample of 964 Danish Gazelle firms. Our study finds a clear positive relationship between growth and subsequent profitability among gazelle firms. Moreover, this relationship is stronger for firms pursuing a broad market strategy rather than a focus or niche strategy. An important managerial implication is that the growth strategy should be clearly integrated with the general strategic orientation of the firm.