An alternating least square approach for the estimation of a SEM based on ordinal variables

The aim of this paper is to propose an approach to quantify the qualitative variables, within Structural Equation Models (SEM), and in particular of PLS-PM. We propose a new algorithm, called Partial Alternating Least Squares Optimal Scaling- Path Modeling (PALSOS-PM), which through an iterative procedure, computes an optimal quantification, for qualitative variables, and structural parameters of the model chosen

Metodi di quantificazione per le variabili qualitative ordinali

In the literature is largely addressed the problem of quantification of qualitative variables, and different proposals, ranging from a simple re-up to a transformation of the full scale of measurement of variables. In this work we discuss some techniques of quantification, used in the Structural Equations (SEM). The SEM allow simultaneous estimation of several causal links between different variables, in particular, we treat the problem of referring to the Partial Least Squares Path Modeling (PLS-PM) for which we present some proposals made in Litterature, based on algorithms of optimal quantification

The Role of Pathological Method and Clearance Definition for the Evaluation of Margin Status after Pancreatoduodenectomy for Periampullary Cancer. Results of a Multicenter Prospective Randomized Trial

Simple SummaryThere is no clear evidence on the most effective method of pathological analysis and clearance definition (0 vs. 1 mm) to define R1 resection after pancreatoduodenectomy (PD). However, several studies showed that the R1 resection is a poor prognostic factor in patients that have undergone PDs for periampullary cancers. In this randomized clinical trial, specimens were randomized with two pathological methods, the Leeds Pathology Protocol (LEEPP) or the conventional method adopted before the study. The 1 mm clearance is the most effective factor in determining R1 rate after PD but only when adopting the LEEP, the R1 resection represents a significant prognostic factor.Background: There is extreme heterogeneity in the available literature on the determination of R1 resection rate after pancreatoduodenectomy (PD); consequently, its prognostic role is still debated. The aims of this multicenter randomized study were to evaluate the effect of sampling and clearance definition in determining R1 rate after PD for periampullary cancer and to assess the prognostic role of R1 resection. Methods: PD specimens were randomized to Leeds Pathology Protocol (LEEPP) (group A) or the conventional method adopted before the study (group B). R1 rate was determined by adopting 0- and 1-mm clearance; the association between R1, local recurrence (LR) and overall survival (OS) was also evaluated. Results. One-hundred-sixty-eight PD specimens were included. With 0 mm clearance, R1 rate was 26.2% and 20.2% for groups A and B, respectively; with 1 mm, R1 rate was 60.7% and 57.1%, respectively (p > 0.05). Only in group A was R1 found to be a significant prognostic factor: at 0 mm, median OS was 36 and 20 months for R0 and R1, respectively, while at 1 mm, median OS was not reached and 30 months. At multivariate analysis, R1 resection was found to be a significant prognostic factor independent of clearance definition only in the case of the adoption of LEEPP. Conclusions. The 1 mm clearance is the most effective factor in determining the R1 rate after PD. However, the pathological method is crucial to accurately evaluate its prognostic role: only R1 resections obtained with the adoption of LEEPP seem to significantly affect prognosis

Guidelines of the Italian Society of Videosurgery in Infancy for the minimally invasive treatment of the ureteropelvic-junction obstruction

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Guidelines of the Italian Society of Videosurgery in Infancy for the minimally invasive treatment of pediatric nephrectomy and partial nephrectomy

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Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function

BACKGROUND: Understanding the genetic architecture of cardiac structure and function may help to prevent and treat heart disease. This investigation sought to identify common genetic variations associated with inter-individual variability in cardiac structure and function. METHODS: A GWAS meta-analysis of echocardiographic traits was performed, including 46,533 individuals from 30 studies (EchoGen consortium). The analysis included 16 traits of left ventricular (LV) structure, and systolic and diastolic function. RESULTS: The discovery analysis included 21 cohorts for structural and systolic function traits (n = 32,212) and 17 cohorts for diastolic function traits (n = 21,852). Replication was performed in 5 cohorts (n = 14,321) and 6 cohorts (n = 16,308), respectively. Besides 5 previously reported loci, the combined meta-analysis identified 10 additional genome-wide significant SNPs: rs12541595 near MTSS1 and rs10774625 in ATXN2 for LV end-diastolic internal dimension; rs806322 near KCNRG, rs4765663 in CACNA1C, rs6702619 near PALMD, rs7127129 in TMEM16A, rs11207426 near FGGY, rs17608766 in GOSR2, and rs17696696 in CFDP1 for aortic root diameter; and rs12440869 in IQCH for Doppler transmitral A-wave peak velocity. Findings were in part validated in other cohorts and in GWAS of related disease traits. The genetic loci showed associations with putative signaling pathways, and with gene expression in whole blood, monocytes, and myocardial tissue. CONCLUSION: The additional genetic loci identified in this large meta-analysis of cardiac structure and function provide insights into the underlying genetic architecture of cardiac structure and warrant follow-up in future functional studies. FUNDING: For detailed information per study, see Acknowledgments.This work was supported by a grant from the US National Heart, Lung, and Blood Institute (N01-HL-25195; R01HL 093328 to RSV), a MAIFOR grant from the University Medical Center Mainz, Germany (to PSW), the Center for Translational Vascular Biology (CTVB) of the Johannes Gutenberg-University of Mainz, and the Federal Ministry of Research and Education, Germany (BMBF 01EO1003 to PSW). This work was also supported by the research project Greifswald Approach to Individualized Medicine (GANI_MED). GANI_MED was funded by the Federal Ministry of Education and Research and the Ministry of Cultural Affairs of the Federal State of Mecklenburg, West Pomerania (contract 03IS2061A). We thank all study participants, and the colleagues and coworkers from all cohorts and sites who were involved in the generation of data or in the analysis. We especially thank Andrew Johnson (FHS) for generation of the gene annotation database used for analysis. We thank the German Center for Cardiovascular Research (DZHK e.V.) for supporting the analysis and publication of this project. RSV is a member of the Scientific Advisory Board of the DZHK. Data on CAD and MI were contributed by CARDIoGRAMplusC4D investigators. See Supplemental Acknowledgments for consortium details. PSW, JFF, AS, AT, TZ, RSV, and MD had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis

Proposte metodologiche per la valutazione della soddisfazione degli studenti universitari

Le indagini statistiche, per la valutazione della soddisfazione di un gruppo di individui rispettoad un servizio ricevuto, sono caratterizzate dalla rilevazione e misurazione di indicatori qualitativi (ordinali e nominali) e quantitativi, in modo da poter fornire una descrizione più approfondita del fenomeno, e usufruire delle variabili qualitative per una discriminazione, tra i soggetti, rispetto ad uno o più concetti latenti. Tuttavia allorquando lo scopo dell’indagine è la determinazione di un modello causale, in cui si stimano i legami tra diversi concetti latenti, come un Modello ad Equazioni Strutturali (MES), l’utilizzo di indicatori qualitativi per la stima dei concetti latenti (un concetto latente è definito tale in quanto misurabile solo attraverso un insieme di indicatori di cui è espressione) causa problemi sia per quanto concerne l’inferenza sui parametri del modello, che potrebbero risultare non significativi, sia per quanto riguarda la convergenza dell’algoritmo di stima. L’approccio dei modelli ad equazioni strutturali nasce, infatti, per la sima dei legami tra variabili quantitative, pertanto allorquando si inseriscono indicatori qualitativi, seppur ricodificati, ciò può provocare una non significatività delle stime dei parametri del modello

An algorithm for the treatment of ordinal variables in a SEM model Electronic

In this work we speak about the problem of the treatment of qualitative variables in the SEM models, and in particular in the PLS-PM. In the contest of PLS-PM we propose an algorithm, called Alternating Least Squares Path Modeling (ALS-PM), that allows us to use the qualitative indicators: the algorithm computes the optimal quantification of qualitative indicators, taking into account the relation between variables (manifest and latent). We discuss in this work also about the validation of the model, in particular we propose some indexes that measure the variability explained by the relation of model between manifest and latent variables. We present some real cases of models in which we have qualitative indicators that with the classical approach of PLS-PM can not be included in the model

The evaluation of the quality of the University across aStructural Equations Modeling

The evaluation of quality of Course of Study is an important issue for the University to obtain the accreditation. Many statistical techniques are useful to this scope as multivariate analysis for the estimation of the assonance between latent variables. Sometimes the topic is the determination of the casual relationship between variables. For this reason we are interested to the modelistic approach: we want to develop a model that can represent the mechanism of the generation of effectiveness and efficiency in the Course of Study; for this scope we suggest to applied a Partial Least Squares Path Modeling (PLS-PM), that belongs to the family of Structural Equations Modeling (SEM); it gives the quantification of the relation between the latent concepts. This model allows to individuate the zone of the process of the quality criticism