3,633 research outputs found
Suitable statistical approaches for novel policies: spatial clusters of childcare’s services in Veneto, Italy
No full textMore and more often, policymakers face complex problems that require suitable information obtainable only from the "intelligence of data." This can be obtained by analyzing several data sets (many of high dimension) and adopting suitable, often "sophisticated," statistical models. Here we deal with policies for affordable and quality childcare, essential to balance work and
family life, increase labor market participation, promote gender equality, and fight against fertility decline. Understanding the complex dynamics of demand and supply of childcare services is challenging due to the nature of the data: high-dimensional, complex, and heterogeneous nationwide.
Considering the Italian case, this complexity and heterogeneity are partially due to the lack of governance at the regional level leading to immediate and effective new policies challenging. This paper aims to analyze the multidimensional aspect of the supply-demand of childcare services combination in the Veneto Italian region using a novel statistical approach and an innovative dataset. We apply the regionalization approach (a
clustering method with spatial constraints) to give an immediate picture of childcare services' supply and demand variability. Our empirical findings confirm how the Veneto region is described by many "sub-regional models," providing a preliminary attempt to demonstrate how socio-demographic factors drive these patterns
Work realities and behavioral risk factors in Italy
No full textThe connection between health, work environment, and job characteristics is a relevant issue in public health. However, it is often underexplored due to a lack of reliable data. To address this gap, we have delved into the subject using data from an NCDs-risk factor surveillance system (PASSI). We have examined information collected from respondents regarding their occupations relating to risk factors and health status. The proposed analysis employs text mining and cluster approach for categorical variables to identify sub-populations characterized by different socio-economic situations, risk factors, and job types. Although further analyses are needed to explore the potential of this approach better, initial results are promising. They highlight the practical implications of our findings for public health policies. For example, we found that occupations related to the building industry (for males) and healthcare professions (for females) appear to be associated with higher behavioral risk factors, which could inform targeted interventions
How active is a genetic pathway? Comparative analysis of post-hoc permutation-based methods
No full textProcedures with true discovery guarantee, i.e., methods for simultaneous inference on the True Discovery Proportion (TDP), have become widely popular in many applications. They permit addressing the multiplicity problem while at the same time solving the spatial specificity paradox. Here we propose a comparative analysis of some of the most widely used permutation-based procedures: sumSome, pARI, sansSouci and Notip. We compare their performance on differential gene expression data analysis, where the interest lies in quantifying levels of activation in different pathways
Procrustes analysis for high-dimensional data
No full textThe Procrustes-based perturbation model (Goodall, 1991) allows minimization
of the Frobenius distance between matrices by similarity transformation.
However, it suffers from non-identifiability, critical interpretation of the
transformed matrices, and inapplicability in high-dimensional data. We provide
an extension of the perturbation model focused on the high-dimensional data
framework, called the ProMises (Procrustes von Mises-Fisher) model. The
ill-posed and interpretability problems are solved by imposing a proper prior
distribution for the orthogonal matrix parameter (i.e., the von Mises-Fisher
distribution) which is a conjugate prior, resulting in a fast estimation
process. Furthermore, we present the Efficient ProMises model for the
high-dimensional framework, useful in neuroimaging, where the problem has much
more than three dimensions. We found a great improvement in functional magnetic
resonance imaging (fMRI) connectivity analysis because the ProMises model
permits incorporation of topological brain information in the alignment's
estimation process.Comment: 22 pages, 7 figure
Functional alignment by the "light" approach of the von Mises-Fisher-Procrustes model.
No full textProcrustes-based methods involve the singular value decomposition of a square matrix, leading to polynomial time complexity, and requiring a considerable memory for large-scale problems. Procrustes-based methods are used as functional alignment for fMRI data in the multi-subjects analysis. A high-dimensional matrix expresses the subject’s neural activation, and Procrustes-based methods are infeasible (computationally). The alignment can be conducted only on regions of interest of the brain. We proposed a “light” version of the Procrustes-based methods. A semi-orthogonal transformation reduces the matrices’ dimension before applying the Procrustes alignment, maintaining the variability of the matrix that enters in the decomposition step. fMRI application shows a low decrease in predictive performance
Valid inference for group analysis of functionally aligned fMRI images
No full textFunctional magnetic resonance imaging (fMRI) data require preprocessing steps before statistical analysis. Multi-subjects fMRI studies are complicated: the brain’s anatomical and functional structure varies across subjects. Anatomical alignment does not capture the functional variability across subjects; the functional alignment is then applied. Generally, group analysis on functionally aligned fMRI data refers to between-subject classification. We propose an inference group analysis arguing that using functional aligned images based on Procrustes transformation
does not affect type I error
Analysis of multimorbidity compression using a latent variable in a mixed mixture model
No full textAbstract Background Multimorbidity, i.e., the co-presence of multiple diseases in an individual, is an increasing concern, particularly as the population ages. Addressing it is critical to improving health status and optimizing healthcare resources. Particularly relevant in this scenario is the concept of multimorbidity compression, i.e., the onset of chronic diseases is delayed more rapidly than the increase in life expectancy. According to this theory, the duration individuals spend in poor health should be shortened. Existing studies have started examining multimorbidity trends, yet often overlook the cumulative burden of multiple diseases. Methods We define the multimorbidity concept as a latent variable estimated with the disease burden described by the disability weights from the Global Burden of Diseases (GBD) project. Using a mixed-mixture model, we analyze the nonlinear relationship between multimorbidity and socioeconomic traits, accounting for zero inflation and spatial variability in Italy. We use twelve years of the surveillance system PASSI data to investigate the multimorbidity compression concept. Results Our findings suggest multimorbidity compression is acting in Italy: severe multimorbidities are increasingly concentrated later in life, indicating a positive impact of healthcare improvements on the quality of life. The phenomenon is observed in both socially advantaged and disadvantaged subpopulations
A novel comorbidity index in Italy based on diseases detected by the surveillance system PASSI and the Global Burden of Diseases disability weights
No full textAbstract Background Understanding comorbidity and its burden characteristics is essential for policymakers and healthcare providers to allocate resources accordingly. However, several definitions of comorbidity burden can be found in the literature. The main reason for these differences lies in the available information about the analyzed diseases (i.e., the target population studied), how to define the burden of diseases, and how to aggregate the occurrence of the detected health conditions. Methods In this manuscript, we focus on data from the Italian surveillance system PASSI, proposing an index of comorbidity burden based on the disability weights from the Global Burden of Disease (GBD) project. We then analyzed the co-presence of ten non-communicable diseases, weighting their burden thanks to the GBD disability weights extracted by a multi-step procedure. The first step selects a set of GBD weights for each disease detected in PASSI using text mining. The second step utilizes an additional variable from PASSI (i.e., the perceived health variable) to associate a single disability weight for each disease detected in PASSI. Finally, the disability weights are combined to form the comorbidity burden index using three approaches common in the literature. Results The comorbidity index (i.e., combined disability weights) proposed allows an exploration of the magnitude of the comorbidity burden in several Italian sub-populations characterized by different socioeconomic characteristics. Thanks to that, we noted that the level of comorbidity burden is greater in the sub-population characterized by low educational qualifications and economic difficulties than in the rich sub-population characterized by a high level of education. In addition, we found no substantial differences in terms of predictive values of comorbidity burden adopting different approaches in combining the disability weights (i.e., additive, maximum, and multiplicative approaches), making the Italian comorbidity index proposed quite robust and general
Enhanced hyperalignment via spatial prior information
No full textFunctional alignment between subjects is an important assumption of
functional magnetic resonance imaging (fMRI) group-level analysis. However, it
is often violated in practice, even after alignment to a standard anatomical
template. Hyperalignment, based on sequential Procrustes orthogonal
transformations, has been proposed as a method of aligning shared functional
information into a common high-dimensional space and thereby improving
inter-subject analysis. Though successful, current hyperalignment algorithms
have a number of shortcomings, including difficulties interpreting the
transformations, a lack of uniqueness of the procedure, and difficulties
performing whole-brain analysis. To resolve these issues, we propose the
ProMises (Procrustes von Mises-Fisher) model. We reformulate functional
alignment as a statistical model and impose a prior distribution on the
orthogonal parameters (the von Mises-Fisher distribution). This allows for the
embedding of anatomical information into the estimation procedure by penalizing
the contribution of spatially distant voxels when creating the shared
functional high-dimensional space. Importantly, the transformations, aligned
images, and related results are all unique. In addition, the proposed method
allows for efficient whole-brain functional alignment. In simulations and
application to data from four fMRI studies we find that ProMises improves
inter-subject classification in terms of between-subject accuracy and
interpretability compared to standard hyperalignment algorithms.Comment: 28 pages, 9 figure
Closed-Based Testing When Multiple Quantile Regressions are Fitted
No full textThis paper addresses the challenge of conducting multiple quantile regressions at different levels and the consequent issue of controlling the familywise error rate (FWER). Current practices in various fields typically involve conducting separate tests for each quantile, leading to a multiplicity problem that often remains unaddressed. We propose a method that integrates the Wald test within a closed-testing procedure to manage multiple tests effectively. We conduct simulation studies across various scenarios to demonstrate the efficacy of our method in controlling the FWER and its power compared to traditional approaches like the Bonferroni correction. Our findings advocate for a more rigorous application of statistical tests in quantile regressions to prevent false discoveries and enhance the reliability of analytical conclusions
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