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.

Looking Yonder : Longitudinal Research and Evaluation of the Yonder Participative Arts-based Learning Model Report 2010-2012

Since 2007, KITE Arts Education Program @ QPAC has been engaged in a series of arts and drama-based experiences for students in selected primary schools on the edges of Brisbane and in regional Queensland. The in-school workshop experiences of the program have culminated in a performance by the children for their school community, parents and carers at the Queensland Performing Arts Centre or a regional cultural venue. In conducting an analysis of the Yonder project, the researcher aimed to provide evidence of outcomes brought about through participation by schools, school staff, students and their communities in the Yonder project. To develop longitudinal data project initiators, participants were interviewed at six-monthly intervals to establish patterns of engagement and participation. The report analyses arts-based workshops conducted by the teacher artist in edge-city Brisbane and a regional centre; interviews with teachers and school administrators from the participating schools; interviews with teacher artist and professional artists; interviews with community partners; teacher professional development workshops; community-based workshops; performance outcomes that were the culminating events of the workshop program; student work samples and student reflections on the program. This document covers data and project outputs from February 2010 to July 2012. There have been five iterations of the Yonder project since its commencement in mid-2009 — three in regional Queensland (February–April 2010; February–May 2011; February–May 2012) and two in edge-city1 Brisbane (July–September 2010; August–October 2011). This report is a result of a research partnership between Queensland Performing Arts Centre and Queensland University of Technology (QUT) Creative Industries Faculty(Drama).

Working for a better life : longitudinal evidence on the predictors of employment among recently arrived refugee migrant men living in Australia

Although a number of studies have investigated the predictors of employment among refugee migrants, there is a dearth of evidence from longitudinal data. This study investigated the cross-sectional and longitudinal predictors of employment among 233 adult refugee men living in South-East Queensland, Australia. Participants were interviewed four times at six-month intervals between 2008 and 2010. Using a conceptual model developed from the literature, Generalised Estimating Equations were used to model the predictors of employment. Over time, the employment rate increased from 44 percent to 56 percent. Region of birth, length of time in Australia, seeking employment through job service providers and informal networks, and owning a car were significant predictors of employment. Contrary to previous research, English language proficiency was not a significant predictor when other variables were controlled for. Recognition of overseas skills and qualifications decreased the chances of finding employment. The policy and program implications are discussed.

Longitudinal Evidence on Educational and Occupational Outcomes Amongst South Sudanese Men from Refugee Backgrounds Living in Urban and Regional Southeast Queensland

Australia has continued to benefit from the human, social and economic capital contributed by immigrant resettlement over many years. Humanitarian entrants have also made significant economic, social and civic contributions to the Australian society. Since 2000, approximately 160,000 people have entered Australia under the refugee and humanitarian resettlement program; around 15% have come from South Sudan and one third of these are adult males. In response to the 2003 evaluation of the Integrated Humanitarian Settlement Strategy (IHSS), which recommended to seek further opportunities to settle humanitarian entrants in regional Australia, the Department of Immigration and Citizenship (DIAC) has since encouraged regional settlement to “address the demand for less skilled labour in regional economies and to assist humanitarian entrants to achieve early employment”. There is evidence, however, of the many challenges faced by humanitarian arrivals living in regional areas. This chapter focuses on the educational and occupational outcomes among 117 South Sudanese adult men from refugee backgrounds. In particular, the chapter uses both cross-sectional (at first interview) and longitudinal data (four interviews with each participant at six-month intervals) to compares outcomes between men living in Brisbane and those living in the Toowoomba–Gatton region in Southeast Queensland.

Shrinkage empirical likelihood estimator in longitudinal analysis with time-dependent covariates-application to modeling the health of Filipino children

The method of generalized estimating equations (GEE) is a popular tool for analysing longitudinal (panel) data. Often, the covariates collected are time-dependent in nature, for example, age, relapse status, monthly income. When using GEE to analyse longitudinal data with time-dependent covariates, crucial assumptions about the covariates are necessary for valid inferences to be drawn. When those assumptions do not hold or cannot be verified, Pepe and Anderson (1994, Communications in Statistics, Simulations and Computation 23, 939–951) advocated using an independence working correlation assumption in the GEE model as a robust approach. However, using GEE with the independence correlation assumption may lead to significant efficiency loss (Fitzmaurice, 1995, Biometrics 51, 309–317). In this article, we propose a method that extracts additional information from the estimating equations that are excluded by the independence assumption. The method always includes the estimating equations under the independence assumption and the contribution from the remaining estimating equations is weighted according to the likelihood of each equation being a consistent estimating equation and the information it carries. We apply the method to a longitudinal study of the health of a group of Filipino children.

Four years on: Are the gazelles still running? A longitudinal study of firm performance after a period of rapid growth

This article examines variations in performance between fast-growth – the so-called gazelle – firms. Specifically, we investigate how the level of growth affects future profitability and how this relationship is moderated by firm strategy. Hypotheses are developed regarding the moderated growth–profitability relationship and are tested using longitudinal data from a sample of 964 Danish gazelle firms. We find a positive relationship between growth and profitability among gazelle firms. This relationship is moderated, however, by market strategy; it is stronger for firms pursuing a broad market strategy rather than a niche strategy. This study contributes to the current literature by providing a more nuanced view of the growth–profitability relationship and investigating the potential for the future performance of gazelle firms.

Diabetes mellitus and the risk of depressive and anxiety disorders in Australian women: A longitudinal study

Background Longitudinal studies examining the risk of depressive and anxiety disorders associated with diabetes are limited. This study examined the association between diabetes and the risk of depressive and anxiety disorders in Australian women using longitudinal data. Methods Datawere froma sample of women who were part of anAustralian pregnancy and birth cohort study. Data comprised self-reported diabetes mellitus and the subsequent reporting of depressive and anxiety disorders. Mood disorders were assessed according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, obtained from participants using Composite International Diagnostic Interview (CIDI)-Auto (WHO WMH-CIDI CAPI, version 21.1.3). Multiple regression models with adjustment for important covariates were used. Results Women with diabetes had a higher lifetime prevalence of any depressive and/or anxiety disorder than women without diabetes. About 3 in 10 women with diabetes experienced a lifetime event of any depressive disorder, while 1 in 2 women with diabetes experienced a lifetime event of any anxiety disorder. In prospective analyses, diabetes was only significantly associated with a 30-day episode of any anxiety disorder (odds ratio [OR] 1.53, 95% confidence interval [CI] 1.09–2.15). In the case of lifetime disorders, diabetes was significantly associated with any depressive disorder (OR 1.37, 95% CI 1.03–1.84), major depressive disorder (OR 1.36, 95% CI 1.01–1.85), and posttraumatic stress disorder (OR 1.42, 95% CI 1.01–2.02). Conclusions The findings suggest that the presence of diabetes is a significant risk factor for women experiencing current anxiety disorders. However, in the case of depression, the association with diabetes only held for women who had experienced past episodes, there was no association with current depression. This suggests that the evidence is not strong enough to support a direct effect of diabetes as a cause of mood disorders.