Smooth-car mixed models for spatial count data

Penalized splines (P-splines) and individual random effects are used for the analysis of spatial count data. P-splines are represented as mixed models to give a unified approach to the model estimation procedure. First, a model where the spatial variation is modelled by a two-dimensional P-spline at the centroids of the areas or regions is considered. In addition, individual area-effects are incorporated as random effects to account for individual variation among regions. Finally, the model is extended by considering a conditional autoregressive (CAR) structure for the random effects, these are the so called “Smooth-CAR” models, with the aim of separating the large-scale geographical trend, and local spatial correlation. The methodology proposed is applied to the analysis of lip cancer incidence rates in Scotland.Mixed models, P-splines, Overdispersion, Negative Binomial, PQL, CAR models, Scottish lip cancer data

Goodness of fit in models for mortality data

Mortality data on an aggregate level are characterized by very large sample sizes. For this reason, uninformative outcomes are evident in common Goodness-of-Fit measures. In this paper we propose a new measure that allows comparison of different mortality models even for large sample sizes. Particularly, we develop a measure which uses a null model specifically designed for mortality data. Several simulation studies and actual applications will demonstrate the performances of this new measure with special emphasis on demographic models and Pspline approach.Goodness of fit, P-splines, R2, Mortality

Modelling spatial trends and local competition effects using semiparametric additive models

The aim of this project was to develop a joint approach to the estimation of spatial trends and competition effects in agricultural field trials. We chose to model the trend by means of a semi parametric model and to extend this class of models to include any number of smooth terms. Explicit expressions for the linear and smooth parts of the model are derived. Two approximations to the standard errors of the linear part are presented and compared. We discuss graphical methods for the initial identification of spatial structure in the data and propose more formal procedures to select the degree of smoothing and to test for the significance of treatment effects. We review the methodology already developed for competition models and improve the fitting procedure by calculating exact adjustments of the profile likelihood for a class of normal regression models. Classical competition models are extended to allow for the estimation of spatial trends via one or more linear smoothers. Methods to estimate the smoothing parameter in the presence of competition were derived. However, we have established that this approach needs to be extended to include correlated errors before it is complete. A mixed model approach to competition was also investigated. The analysis of the data from two agricultural trials grown at SCRI indicated that SAMs provide a flexible framework for identifying underlying trends in field trials. They generally improve precision of the treatment estimates and they enable spatial trends to be easily visualised. Competition between neighbouring plots was also identified

Elementos potenciadores de la satisfacción por compasión en profesionales sociosanitarios

Objective: analysis of adult attachment styles, self-compassion, vocation, health care demands, caring satisfaction, job satisfaction and burnout on compassion satisfaction (CS) in healthcare professionals.  Method: An online questionnaire was answered by 480 assistentially active healthcare professionals. Variables collected were socio-demographic, work experience, burnout and compassion satisfaction (ProQoL Quality of Life Questionnaire), self-compassion (by Neff), and attachment styles (Relation Questionnaire). Correlations, mean differences, multiple linear regression (MLR) and qualitative analysis of the emotional description of the job were analyzed. Results: A 79,6% (382) were women, age average 44,6 (DT = 10,86). CS score resulted significantly (p<,001) higher than burnout. Model for CS explained a 51.5% of the variability (correctedR2=,515); as predictor variables (p<,001), caring satisfaction (Beta=,309), vocation (Beta=,184), self-kindness (Beta=,158) and burnout (Beta=,306). Conclusions: compassion satisfaction is directly influenced by people caring satisfaction, vocation, self-kindness and absence of burnout. Indirectly also, by mindfulness capacity, feelings of common humanity, secure attachment style and satisfaction with the team. These also are preventing factors from burnout, that is directly related to concerned and fearful attachment styles and lack of self-compassion; self-judgement, over-identification and isolation.Objetivo: análisis de la influencia de estilos de apego, autocompasión, vocación, demanda asistencial, satisfacción de cuidar, satisfacción con el trabajo y burnout sobre la satisfacción por compasión (SC) en profesionales sociosanitarios. Método: Contestaron al cuestionario online 480 profesionales sociosanitarios asistencialmente activos. Se recogieron variables sociodemográficas, de experiencia laboral, burnout y satisfacción por compasión (Cuestionario de Calidad de Vida ProQoL), autocompasión (de Neff) y estilos de apego (Cuestionario de Relación). Se analizaron correlaciones, diferencias de medias, regresión lineal múltiple (RLM) y análisis cualitativo de la descripción emocional del trabajo. Resultados: El 79,6% (382) fueron mujeres, edad media de 44,6 (DT=10,86). Resultó significativamente (p<,001) mayor la puntuación SC que burnout. El modelo de SC explicó un 51,5% de la varianza (R2corregida=0,515); como variables predictoras (p<,001), satisfacción de cuidar personas (Beta=,309), vocación (Beta=,184), autoamabilidad (Beta=,158) y burnout (Beta=-,306).  Conclusiones: sobre la satisfacción por compasión, directamente influye la satisfacción de cuidar personas, vocación, autoamabilidad y ausencia de burnout. Indirectamente también, la capacidad de atención plena, sentimientos de humanidad compartida, vínculo de apego seguro y satisfacción con el equipo de trabajo. También son factores protectores ante burnout, que se relaciona directamente con estilos de apego preocupado, temeroso y falta de autocompasión; autocrítica, sobreidentificación y aislamiento

Derivative estimation for longitudinal data analysis:Examining features of blood pressure measured repeatedly during pregnancy

Estimating velocity and acceleration trajectories allows novel inferences in the field of longitudinal data analysis, such as estimating change regions rather than change points, and testing group effects on nonlinear change in an outcome (ie, a nonlinear interaction). In this article, we develop derivative estimation for 2 standard approachespolynomial mixed models and spline mixed models. We compare their performance with an established methodprincipal component analysis through conditional expectation through a simulation study. We then apply the methods to repeated blood pressure (BP) measurements in a UK cohort of pregnant women, where the goals of analysis are to (i) identify and estimate regions of BP change for each individual and (ii) investigate the association between parity and BP change at the population level. The penalized spline mixed model had the lowest bias in our simulation study, and we identified evidence for BP change regions in over 75% of pregnant women. Using mean velocity difference revealed differences in BP change between women in their first pregnancy compared with those who had at least 1 previous pregnancy. We recommend the use of penalized spline mixed models for derivative estimation in longitudinal data analysis

Sustained proliferation in cancer: mechanisms and novel therapeutic targets

Proliferation is an important part of cancer development and progression. This is manifest by altered expression and/or activity of cell cycle related proteins. Constitutive activation of many signal transduction pathways also stimulates cell growth. Early steps in tumor development are associated with a fibrogenic response and the development of a hypoxic environment which favors the survival and proliferation of cancer stem cells. Part of the survival strategy of cancer stem cells may manifested by alterations in cell metabolism. Once tumors appear, growth and metastasis may be supported by overproduction of appropriate hormones (in hormonally dependent cancers), by promoting angiogenesis, by undergoing epithelial to mesenchymal transition, by triggering autophagy, and by taking cues from surrounding stromal cells. A number of natural compounds (e.g., curcumin, resveratrol, indole-3-carbinol, brassinin, sulforaphane, epigallocatechin-3-gallate, genistein, ellagitannins, lycopene and quercetin) have been found to inhibit one or more pathways that contribute to proliferation (e.g., hypoxia inducible factor 1, nuclear factor kappa B, phosphoinositide 3 kinase/Akt, insulin-like growth factor receptor 1, Wnt, cell cycle associated proteins, as well as androgen and estrogen receptor signaling). These data, in combination with bioinformatics analyses, will be very important for identifying signaling pathways and molecular targets that may provide early diagnostic markers and/or critical targets for the development of new drugs or drug combinations that block tumor formation and progression