The dyadic regulation approach of coping and illness representations in female cancer patients and their partners

PurposeAdjustment to any illness is a ‘dyadic' process whereby patients and their partners mutually determine each other's perceptions, behaviours, and well-being. The present study explored the association between dyadic coping strategies and illness representations in newly diagnosed female cancer patients and their partners.MethodsThe sample consisted of 92 female cancer patient-partner pairs from 3 oncology hospitals in Greece and Cyprus. The Actor Partner Interdependence Model was applied to test for dyadic regulation effects.ResultsThe findings revealed that patients' evaluations of dyadic coping were related to their own illness representations and, in some cases, to partners' illness representations of control. However, partner evaluations of dyadic coping were not associated with either patients' or their own illness representations. Relationship satisfaction did not moderate the relationship between dyadic coping and illness representations.ImplicationsThe study suggests that patients' perceptions of support provided by themselves and their partners play a significant role in shaping their illness representations. Future research could delve into the underlying reasons for the observed differences in the impact of dyadic coping on illness representations between patients and partners, considering factors such as gender roles and specific gender-related issues

Determining the likely place of HIV acquisition for migrants in Europe combining subject-specific information and biomarkers data

In most HIV-positive individuals, infection time is only known to lie between the time an individual started being at risk for HIV and diagnosis time. However, a more accurate estimate of infection time is very important in certain cases. For example, one of the objectives of the Advancing Migrant Access to Health Services in Europe (aMASE) study was to determine if HIV-positive migrants, diagnosed in Europe, were infected pre- or post-migration. We propose a method to derive subject-specific estimates of unknown infection times using information from HIV biomarkers' measurements, demographic, clinical, and behavioral data. We assume that CD4 cell count (CD4) and HIV-RNA viral load trends after HIV infection follow a bivariate linear mixed model. Using post-diagnosis CD4 and viral load measurements and applying the Bayes' rule, we derived the posterior distribution of the HIV infection time, whereas the prior distribution was informed by AIDS status at diagnosis and behavioral data. Parameters of the CD4-viral load and time-to-AIDS models were estimated using data from a large study of individuals with known HIV infection times (CASCADE). Simulations showed substantial predictive ability (e.g. 84% of the infections were correctly classified as pre- or post-migration). Application to the aMASE study ( n = 2009) showed that 47% of African migrants and 67% to 72% of migrants from other regions were most likely infected post-migration. Applying a Bayesian method based on bivariate modeling of CD4 and viral load, and subject-specific information, we found that the majority of HIV-positive migrants in aMASE were most likely infected after their migration to Europe

Management earnings forecasts and IPO performance: evidence of a regime change

Companies undertaking initial public offerings (IPOs) in Greece were obliged to include next-year profit forecast in their prospectuses, until the regulation changed in 2001 to voluntary forecasting. Drawing evidence from IPOs issued in the period 1993–2015, this is the first study to investigate the effect of disclosure regime on management earnings forecasts and IPO long-term performance. The findings show mainly positive forecast errors (forecasts are lower than actual earnings) and higher long-term returns during the mandatory period, suggesting that the mandatory disclosure requirement causes issuers to systematically bias profit forecasts downwards as they opt for the safety of accounting conservatism. The mandatory disclosure requirement artificially improves IPO share performance. Overall, our results show that mandatory disclosure of earnings forecasts can impede capital market efficiency once it goes beyond historical financial information to involve compulsory projections of future performance

Long-term Performance of Greek IPOs

We analyse the long-run performance of 254 Greek IPOs that were listed during the period 1994-2002, computing buy-and-hold abnormal returns (BHAR) and cumulative abnormal returns (CAR) over 36 months of secondary market performance. The empirical results differ from international evidence and reveal long-term overperformance that continues for a substantial interval after listing. Measuring these returns in calendar time, we find statistical significance with several of the benchmarks employed. We also find that long-term overperformance is a feature of the mass of IPOs conducted during a pronounced IPO wave. Cross-sectional regressions of long-run performance disclose several significant factors. The study demonstrates that although Greek IPOs overperform the market for a longer period, underperformance eventually emerges, in line with much international evidence. Our interpretation is that the persistence of overperformance over a significant interval is due to excessive supply of issues during the 'hot IPO period'. Results associated with pricing during the 'hot IPO period' indicate positive short- (1-year), medium- (2-year) and negative long-term (3-year) performance. © 2010 Blackwell Publishing Ltd

Long-term Performance of Greek IPOs

We analyse the long-run performance of 254 Greek IPOs that were listed during the period 1994–2002, computing buy-and-hold abnormal returns (BHAR)and cumulative abnormal returns (CAR) over 36 months of secondary market performance. The empirical results differ from international evidence and reveal long-term overperformance that continues for a substantial interval after listing. Measuring these returns in calendar time, we find statistical significance with several of the benchmarks employed. We also find that long-term overperformance is a feature of the mass of IPOs conducted during a pronounced IPO wave. Cross-sectional regressions of long-run performance disclose several significant factors. The study demonstrates that although Greek IPOs overperform the market for a longer period, underperformance eventually emerges, in line with much international evidence. Our interpretation is that the persistence of overperformance over a significant interval is due to excessive supply of issues during the ‘hot IPO period’. Results associated with pricing during the ‘hot IPO period’ indicate positive short- (1-year), medium- (2-year) and negative long-term (3-year) performance

Misspecifying the covariance structure in a linear mixed model under MAR drop-out

Misspecification of the covariance structure in a linear mixed model (LMM) can lead to biased population parameters' estimates under MAR drop-out. In our motivating example of modeling CD4 cell counts during untreated HIV infection, random intercept and slope LMMs are frequently used. In this article, we evaluate the performance of LMMs with specific covariance structures, in terms of bias in the fixed effects estimates, under specific MAR drop-out mechanisms, and adopt a Bayesian model comparison criterion to discriminate between the examined approaches in real-data applications. We analytically show that using a random intercept and slope structure when the true one is more complex can lead to seriously biased estimates, with the degree of bias depending on the magnitude of the MAR drop-out. Under misspecified covariance structure, we compare in terms of induced bias the approach of adding a fractional Brownian motion (BM) process on top of random intercepts and slopes with the approach of using splines for the random effects. In general, the performance of both approaches was satisfactory, with the BM model leading to smaller bias in most cases. A simulation study is carried out to evaluate the performance of the proposed Bayesian criterion in identifying the model with the correct covariance structure. Overall, the proposed method performs better than the AIC and BIC criteria under our specific simulation setting. The models under consideration are applied to real data from the CASCADE study; the most plausible model is identified by all examined criteria. © 2020 John Wiley & Sons, Ltd

CONTENTS i Contents

This research report describes the design and implementation of AtmSim, a simulator for entire ATM networks. The simulator is designed to be the basic building block of an integrated system that can be used for the design and analysis of ATM networks. The basic objectives of this work is the development of a simulation infrustructure were, (i) different ATM cell scheduling, congestion control and resource allocation algorithms can be developed, tested, analyzed and studied, (ii) the detailed physical network topology can be given as input, (iii) the user traffic sources can, either be obtained from traces of actual network traffic, or generated from formal distributions by the simulator itself, (iv) an extensive suite of results can be collected for different parts of the system under study, including user-oriented and system oriented performance measures, (v) the collected results have high accuracy and can be saved in formats that can be directly fed into statistical postoprocessing ..