What Are Effective Program Characteristics of Self-Management Interventions in Patients With Heart Failure? : An Individual Patient Data Meta-analysis

Background To identify those characteristics of self-management interventions in patients with heart failure (HF) that are effective in influencing health-related quality of life, mortality, and hospitalizations. Methods and Results Randomized trials on self-management interventions conducted between January 1985 and June 2013 were identified and individual patient data were requested for meta-analysis. Generalized mixed effects models and Cox proportional hazard models including frailty terms were used to assess the relation between characteristics of interventions and health-related outcomes. Twenty randomized trials (5624 patients) were included. Longer intervention duration reduced mortality risk (hazard ratio 0.99, 95% confidence interval [CI] 0.97–0.999 per month increase in duration), risk of HF-related hospitalization (hazard ratio 0.98, 95% CI 0.96–0.99), and HF-related hospitalization at 6 months (risk ratio 0.96, 95% CI 0.92–0.995). Although results were not consistent across outcomes, interventions comprising standardized training of interventionists, peer contact, log keeping, or goal-setting skills appeared less effective than interventions without these characteristics. Conclusion No specific program characteristics were consistently associated with better effects of self-management interventions, but longer duration seemed to improve the effect of self-management interventions on several outcomes. Future research using factorial trial designs and process evaluations is needed to understand the working mechanism of specific program characteristics of self-management interventions in HF patients.

Validación de criterio concurrente del sistema de observación de tiempo de instrucción de condición física – SOFIT- para la medición de la actividad física durante las clases de educación física en escolares de 1º A 9º de Bogotá, Colombia

Determinar la validez concurrente del Sistema de Observación de Tiempo de Instrucción de Condición Física (SOFIT) a través de acelerometría, como método para medir los niveles de actividad física (AF) de los escolares de 1º a 9º durante la clase de educación física en tres colegios públicos de Bogotá, Colombia. Estudio transversal entre Octubre de 2014 y Marzo de 2015. La medición se realizó en tres colegios públicos de Bogotá. Participaron 48 estudiantes (25 niñas; 23 niños), entre 5 y 17 años, seleccionados de acuerdo al protocolo de SOFIT. El resultado se categoriza en porcentaje de tiempo en comportamiento sedentario, AF moderada, AF vigorosa, y AF moderada a vigorosa. Se validó utilizando como patrón de oro la acelerometría en las mismas categorías. Se realizó diferencia de medias, regresión lineal y modelo de efectos fijos. La correlación entre SOFIT y acelerometría fue buena para AF moderada (rho=,958; p=0,000), AF vigorosa (rho=,937; p=0,000) y AF de moderada a vigorosa (rho=0,962; p=0,000). Al igual que utilizando un modelo de efectos fijos, AF moderada (β1=0,92; p=0,00), vigorosa (β1=0,94; p=0,00) y AF de moderada a vigorosa (β1=0,95; p=0,00), mostrando ausencia de diferencias significativas entre los dos métodos para la medición de los niveles de AF. El comportamiento sedentario correlacionó positivamente en Spearman (rho=,0965; p=0,000), El sistema SOFIT demostró ser válido para medir niveles de AF en clases de educación física, tras buena correlación y concordancia con acelerometría. SOFIT es un instrumento de fácil acceso y de bajo costo para la medición de la AF durante las clases de educación física en el contexto escolar y se recomienda su uso en futuros estudios.

Predicting random effects with an expanded finite population mixed model

Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Recently, Stanek and Singer [Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 119-130] developed best linear unbiased predictors (BLUP) under a finite population mixed model that outperform BLUPs from mixed models and superpopulation models. Their setup, however, does not allow for unequally sized clusters. To overcome this drawback, we consider an expanded finite population mixed model based on a larger set of random variables that span a higher dimensional space than those typically applied to such problems. We show that BLUPs for linear combinations of the realized cluster means derived under such a model have considerably smaller mean squared error (MSE) than those obtained from mixed models, superpopulation models, and finite population mixed models. We motivate our general approach by an example developed for two-stage cluster sampling and show that it faithfully captures the stochastic aspects of sampling in the problem. We also consider simulation studies to illustrate the increased accuracy of the BLUP obtained under the expanded finite population mixed model. (C) 2007 Elsevier B.V. All rights reserved.

Estimation in a multiplicative mixed model involving a genetic relationship matrix.

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.

Small area estimation using linear mixed models with heterogeneous covariance structures of random effects

The problem of Small Area Estimation is about how to produce reliable estimates of domain characteristics when the sample sizes within the domain is very small ou even zero.

A mixed model QTL analysis for sugarcane multiple-harvest-location trial data

Sugarcane-breeding programs take at least 12 years to develop new commercial cultivars. Molecular markers offer a possibility to study the genetic architecture of quantitative traits in sugarcane, and they may be used in marker-assisted selection to speed up artificial selection. Although the performance of sugarcane progenies in breeding programs are commonly evaluated across a range of locations and harvest years, many of the QTL detection methods ignore two- and three-way interactions between QTL, harvest, and location. In this work, a strategy for QTL detection in multi-harvest-location trial data, based on interval mapping and mixed models, is proposed and applied to map QTL effects on a segregating progeny from a biparental cross of pre-commercial Brazilian cultivars, evaluated at two locations and three consecutive harvest years for cane yield (tonnes per hectare), sugar yield (tonnes per hectare), fiber percent, and sucrose content. In the mixed model, we have included appropriate (co)variance structures for modeling heterogeneity and correlation of genetic effects and non-genetic residual effects. Forty-six QTLs were found: 13 QTLs for cane yield, 14 for sugar yield, 11 for fiber percent, and 8 for sucrose content. In addition, QTL by harvest, QTL by location, and QTL by harvest by location interaction effects were significant for all evaluated traits (30 QTLs showed some interaction, and 16 none). Our results contribute to a better understanding of the genetic architecture of complex traits related to biomass production and sucrose content in sugarcane.

Posterior Simulation in the Generalized Linear Model with Semiparmetric Random Effects

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP) model, normal base measures and Gibbs sampling procedures based on the Pólya urn scheme are often used to simulate posterior draws. These algorithms are applicable in the conjugate case when (for a normal base measure) the likelihood is normal. In the non-conjugate case, the algorithms proposed by MacEachern and Müller (1998) and Neal (2000) are often applied to generate posterior samples. Some common problems associated with simulation algorithms for non-conjugate MDP models include convergence and mixing difficulties. This paper proposes an algorithm based on the Pólya urn scheme that extends the Gibbs sampling algorithms to non-conjugate models with normal base measures and exponential family likelihoods. The algorithm proceeds by making Laplace approximations to the likelihood function, thereby reducing the procedure to that of conjugate normal MDP models. To ensure the validity of the stationary distribution in the non-conjugate case, the proposals are accepted or rejected by a Metropolis-Hastings step. In the special case where the data are normally distributed, the algorithm is identical to the Gibbs sampler.

A Diagnostic Test for the Mixing Distribution in a Generalised Linear Mixed Model

We introduce a diagnostic test for the mixing distribution in a generalised linear mixed model. The test is based on the difference between the marginal maximum likelihood and conditional maximum likelihood estimates of a subset of the fixed effects in the model. We derive the asymptotic variance of this difference, and propose a test statistic that has a limiting chi-square distribution under the null hypothesis that the mixing distribution is correctly specified. For the important special case of the logistic regression model with random intercepts, we evaluate via simulation the power of the test in finite samples under several alternative distributional forms for the mixing distribution. We illustrate the method by applying it to data from a clinical trial investigating the effects of hormonal contraceptives in women.

The Effects of Living in Segregated vs. Mixed Areas in Northern Ireland: A Simultaneous Analysis of Contact and Threat Effects in the Context of Micro-Level Neighbourhoods

This study examines the consequences of living in segregated and mixed neighbourhoods on ingroup bias and offensive action tendencies, taking into consideration the role of intergroup experiences and perceived threat. Using adult data from a cross-sectional survey in Belfast, Northern Ireland, we tested a model that examined the relationship between living in segregated (N = 396) and mixed (N = 562) neighbourhoods and positive contact, exposure to violence, perceived threat and outgroup orientations. Our results show that living in mixed neighbourhoods was associated with lower ingroup bias and reduced offensive action tendencies. These effects were partially mediated by positive contact. However, our analysis also shows that respondents living in mixed neighbourhoods report higher exposure to political violence and higher perceived threat to physical safety. These findings demonstrate the importance of examining both social experience and threat perceptions when testing the relationship between social environment and prejudice.

The effects of endothelin-1 on coronary perfusion pressure during cardiopulmonary resuscitation in a canine model

OBJECTIVE To study the hemodynamic effects of exogenously administered endothelin-1 (ET-1), a peptide produced by endothelial cells with potent non-adrenergically mediated vasoconstrictor properties. METHODS A prospective drug intervention study was carried out in a resuscitation research laboratory. Fifteen mixed-breed dogs were anesthetized and instrumented for hemodynamic monitoring. Asphyxia arrest was produced by clamping the endotracheal tube. Hemodynamic data were collected continuously. Following loss of aortic fluctuations monitored by thoracic aortic catheter, the animals remained in pulseless electrical activity (PEA) for 10 minutes. After 10 minutes of no-flow PEA, closed-chest CPR was begun and the animals were randomized to one of three treatment groups (EPI, 0.02 mg/kg epinephrine IV every 3 minutes; ENDO, 100 micrograms ET-1 IV at 0 minutes; and EPI/ENDO, a combination of the EPI and ENDO treatments). RESULTS ENDO and EPI alone produced similar coronary perfusion pressures (CPPs). The EPI/ENDO combination produced significantly improved CPP compared with that of either EPI or ENDO alone. In the EPI group, the best mean CPP was 16 +/- 14 mm Hg and occurred 7 minutes after drug administration. In the ENDO group, the best mean CPP was 28 +/- 7 mm Hg and occurred 13 minutes after drug administration. In the EPI/ENDO combination group, the best mean CPP was 61 +/- 37 mm Hg and occurred 7 minutes after drug administration (p < 0.05 compared with the EPI and ENDO groups alone). CONCLUSION ET-1 is a potent vasoconstrictor. The combination of EPI and ENDO significantly improved CPP compared with that for either agent alone. ET-1 should be investigated further as a vasoconstrictor in cardiac arrest.

A randomized trial of the effects of the noble gases helium and argon on neuroprotection in a rodent cardiac arrest model.

BACKGROUND The noble gas xenon is considered as a neuroprotective agent, but availability of the gas is limited. Studies on neuroprotection with the abundant noble gases helium and argon demonstrated mixed results, and data regarding neuroprotection after cardiac arrest are scant. We tested the hypothesis that administration of 50% helium or 50% argon for 24 h after resuscitation from cardiac arrest improves clinical and histological outcome in our 8 min rat cardiac arrest model. METHODS Forty animals had cardiac arrest induced with intravenous potassium/esmolol and were randomized to post-resuscitation ventilation with either helium/oxygen, argon/oxygen or air/oxygen for 24 h. Eight additional animals without cardiac arrest served as reference, these animals were not randomized and not included into the statistical analysis. Primary outcome was assessment of neuronal damage in histology of the region I of hippocampus proper (CA1) from those animals surviving until day 5. Secondary outcome was evaluation of neurobehavior by daily testing of a Neurodeficit Score (NDS), the Tape Removal Test (TRT), a simple vertical pole test (VPT) and the Open Field Test (OFT). Because of the non-parametric distribution of the data, the histological assessments were compared with the Kruskal-Wallis test. Treatment effect in repeated measured assessments was estimated with a linear regression with clustered robust standard errors (SE), where normality is less important. RESULTS Twenty-nine out of 40 rats survived until day 5 with significant initial deficits in neurobehavioral, but rapid improvement within all groups randomized to cardiac arrest. There were no statistical significant differences between groups neither in the histological nor in neurobehavioral assessment. CONCLUSIONS The replacement of air with either helium or argon in a 50:50 air/oxygen mixture for 24 h did not improve histological or clinical outcome in rats subjected to 8 min of cardiac arrest.

Effects of Surface Forcing on Interannual Variability of the Fall Phytoplankton Bloom in the Gulf of Maine Revealed Using a Process-Oriented Model

SeaWiFS (Sea-viewing Wide Field-of-view Sensor) chlorophyll data revealed strong interannual variability in fall phytoplankton dynamics in the Gulf of Maine, with 3 general features in any one year: (1) rapid chlorophyll increases in response to storm events in fall; (2) gradual chlorophyll increases in response to seasonal wind-and cooling-induced mixing that gradually deepens the mixed layer; and (3) the absence of any observable fall bloom. We applied a mixed-layer box model and a 1-dimensional physical-biological numerical model to examine the influence of physical forcing (surface wind, heat flux, and freshening) on the mixed-layer dynamics and its impact on the entrainment of deep-water nutrients and thus on the appearance of fall bloom. The model results suggest that during early fall, the surface mixed-layer depth is controlled by both wind-and cooling-induced mixing. Strong interannual variability in mixed-layer depth has a direct impact on short-and long-term vertical nutrient fluxes and thus the fall bloom. Phytoplankton concentrations over time are sensitive to initial pre-bloom profiles of nutrients. The strength of the initial stratification can affect the modeled phytoplankton concentration, while the timing of intermittent freshening events is related to the significant interannual variability of fall blooms.

An empirical mixed model to quantify climate influence on the growth of Pinus halepensis Mill. stands in South-Eastern Spain

The influence of climate on forest stand composition, development and growth is undeniable. Many studies have tried to quantify the effect of climatic variables on forest growth and yield. These works become especially important because there is a need to predict the effects of climate change on the development of forest ecosystems. One of the ways of facing this problem is the inclusion of climatic variables into the classic empirical growth models. The work has a double objective: (i) to identify the indicators which best describe the effect of climate on Pinus halepensis growth and (ii) to quantify such effect in several scenarios of rainfall decrease which are likely to occur in the Mediterranean area. A growth mixed model for P. halepensis including climatic variables is presented in this work. Growth estimates are based on data from the Spanish National Forest Inventory (SNFI). The best results are obtained for the indices including rainfall, or rainfall and temperature together, with annual precipitation, precipitation effectiveness, Emberger?s index or free bioclimatic intensity standing out among them. The final model includes Emberger?s index, free bioclimatic intensity and interactions between competition and climate indices. The results obtained show that a rainfall decrease about 5% leads to a decrease in volume growth of 5.5?7.5% depending on site quality.

Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.

Assessment of University External Internships. Towards a Mixed Model for Measuring the Acquisition of Skills and the Satisfaction of Parties Involved

The continuous improvement of management and assessment processes for curricular external internships has led a group of university teachers specialised in this area to develop a mixed model of measurement that combines the verification of skill acquisition by those students choosing external internships with the satisfaction of the parties involved in that process. They included academics, educational tutors of companies and organisations and administration and services personnel in the latter category. The experience, developed within University of Alicante, has been carried out in the degrees of Business Administration and Management, Business Studies, Economics, Advertising and Public Relations, Sociology and Social Work, all part of the Faculty of Economics and Business. By designing and managing closed standardised interviews and other research tools, validated outside the centre, a system of continuous improvement and quality assurance has been created, clearly contributing to the gradual increase in the number of students with internships in this Faculty, as well as to the improvement in satisfaction, efficiency and efficacy indicators at a global level. As this experience of educational innovation has shown, the acquisition of curricular knowledge, skills, abilities and competences by the students is directly correlated with the satisfaction of those parties involved in a process that takes the student beyond the physical borders of a university campus. Ensuring the latter is a task made easier by the implementation of a mixed assessment method, combining continuous and final assessment, and characterised by its rigorousness and simple management. This report presents that model, subject in turn to a persistent and continuous control, a model all parties involved in the external internships are taking part of. Its short-term results imply an increase, estimated at 15% for the last course, in the number of students choosing curricular internships and, for the medium and long-term, a major interweaving between the academic world and its social and productive environment, both in the business and institutional areas. The potentiality of this assessment model does not lie only in the quality of its measurement tools, but also in the effects from its use in the various groups and in the actions that are carried out as a result of its implementation and which, without any doubt and as it is shown below, are the real guarantee of a continuous improvement.