1,721,002 research outputs found
METODI STATISTICI PER L'ANALISI E LA PREVISIONE DELLA MORTALITA' PER TUMORE
Full text linkThe introduction of time series modeling techniques made analyzing the different factors underlying the changes in mortality and incidence rates over time possible, both for analytic and predictive purposes. Age-period-cohort analyses contribute to the etiologic purpose of descriptive epidemiology making inference from the group to the individual possible. These refer to a family of statistical techniques that study the temporal trends of outcomes, such as mortality an incidence, in terms of three temporal variables: subject age, calendar period and the subject's birth cohort. Useful as it is, the age-period-cohort model is marred by a structural problem of identifiability: the variables of age, period and cohort have an exact linear dependence, i.e. "age = period - cohort".
Predicting a future event is a complex and insidious process, however, it is a useful endeavor in most human activities. The information gained on probable future trends, even if unreliable or imprecise is highly valuable. Predicted future cancer incidence and mortality rates are essential tools for both epidemiology and health planning.
Numerous methods to carry out age-period-cohort analysis are described in the literature, three of these are illustrated in detail and compared by applying them to real data (WHO mortality database): a method based on penalized likelihood, one using generalized additive models (GAM) and one based on partial least squares (PLS) techniques.
Predictive analysis techniques are also presented and compared, using observed mortality data. Short term age-period prediction methods based on joinpoint analysis and Bayesian modelling, and a long term technique, which uses a Bayesian age-period-cohort model, are reviewed. In details, predictions through age-period method based on joinpoint analysis are carried out applying linear, Poisson and log-linear regression models.
In the age-period-cohort analysis comparison, the penalized likelihood and GAM methods produce similar results, while effect estimates from the PLS model are noticeably different. These differences can be explained by looking at how the three models solve the issue of perfect collinearity between age, period and cohort parameters. On the one hand, the penalized likelihood and GAM methods use different techniques to distribute the linear drift between the period and cohort effects. The PLS method, on the other hand, solves the identifiability problem by tackling the generalized inverse, minimizing the estimated parameter variance and covariance matrix. Without a formal simulation analysis, comments are limited to stating that the two models based on linear drift distribution are more suitable for epidemiological comparisons, where the effects of age are well defined (as in the case of cancer mortality) and the major problems reside in untangling the period and cohort effects. The PLS model, on the other hand, may hypothetically prove to be a useful method to predict future trends. Age-period-cohort analysis is thus an extremely useful tool in the study of mortality data, particularly for cohort effect analysis, but it should be used with due caution since it is relatively easy to draw erroneous conclusions.
The predictive method comparison shows that estimates from the different models are similar, especially for the Poisson and log-linear models. However, the linear model has a tendency to underestimate, while the other considered models seem to overestimate, particularly as the forecasting time period grew larger. Overall, the Bayesian age-period model seems to be less suitable for short and medium term mortality predictions, while the other models do not show large performance differences. From these limited tests the linear model and the Bayesian age-period-cohort model seem to provide better estimates when mortality values are low, whereas in the case of greater numbers Poisson and log-linear models seem like better choices. Finally, the analyzed data's unknown underlying distribution shape determines which model predicts more successfully. However, all the studied models are appropriate for predicting data over short periods (up to 5 years). While none of them performs well over the medium term. Prediction of future trends will always be a complex and insidious exercise, albeit an extremely useful one, furthermore the obtained estimates should be taken with caution and only regarded as a general indication of potential interest for epidemiology and health planning
Edifici: check up con la termografia, una tecnica d’indagine diagnostica efficace per rilevare il degrado degli edifici per un corretto risanamento architettonico
No full textComparison of age-period-cohort models for the analysis of mortality rates
No full textAge–period–cohort (APC) analyses are a family of statistical techniques to study temporal trends in terms of three related time variables: the subject’s age (A), the calendar period (P) and the subject’s birth cohort (C). APC analysis studies the effects of age, period and cohort simultaneously to disentangle their contributions to the studied outcome. The age, period and cohort variables have an exact linear dependence: A=P-C. This causes an identifiability problem. To overcome this issue, three APC analysis methods from the literature are examined.
Penalised likelihood APC method
This method identifies the solution that minimizes the Euclidean distance between the three two factor models (age-period, age-cohort, cohort-period) by weighing them by their goodness of fit [1].
Generalised additive models (GAM) APC method
Carstensen proposed to use natural splines to smooth the non linear curves of APC models using Holford’s parametrization [2].
Partial Least Squares (PLS) APC method
PLS regression is used with a two-stage procedure [3]. First a factorial method is applied to obtain the PLS components. These are selected to maximize covariance between the outcome and the unobserved factors. Subsequently the unobserved factors are used as regressors. Finally these coefficients are transformed into the familiar parameters of age, period and cohort.
APC analysis is useful to study mortality data, particularly cohort effects, but should be used with caution. Penalised likelihood and GAM methods produce similar results, while the PLS method presents differences. The first two methods use different techniques to distribute the effect of the temporal linear drift between cohort and period factors to solve the identifiability problem, whilst the PLS method solves the problem minimizing the matrix of variances and covariances among the possible estimated parameters in the generalized inverse. From an empirical comparison of the models, we conclude that models based on drift distribution are adequate for epidemiological comparisons, where the problem lies mainly in disentangling the drift effect between cohorts and periods. The PLS model is interesting in projecting future rates.
References
1. Decarli A., La Vecchia C. Rivista Statistica Applicata 1987;20:397-410.
2. Carstensen B. Stat Med 2007;26(15):3018-45.
3. Fukuda K. Stat Comp Simulation 2011;81(12):1871–187
DNA fragmentation in normal development of the human central nervous system: A morphological study during corticogenesis
No full textRefinement of the cell number by programmed cell death is a major morphogenetic mechanism of the developing central nervous system (CNS) in vertebrates including mammals, which determines to a significant degree its mature cytoarchitecture. We have examined the topography and the extent of cell death in different regions of the human CNS prenatally (11 fetuses), and in the early post-natal weeks (three newborns). Attention was focused on the wall of the telencephalon during a relatively short time period (12th-23rd week of gestation), corresponding to the time of major proliferation in the ventricular zone and to the peak of neuronal migration; both these mechanisms are crucial for corticogenesis. The TUNEL method was used, allowing the recognition of cell death because of its ability to label blunt ends of double-stranded DNA breaks. Morphological features of nuclei at different stages of apoptosis were identified, providing better evidence of the extent of the process than histological stains. Cell labelling was seen in either post-mitotic elements in the ventricular zone, or along the migratory pathways in the intermediate zone and subplate at all prenatal ages examined. No apoptotic nuclei were seen in the cortical plate. These findings suggest that apoptotic cell death drives the selection of cells which are committed to play a role during the early stages of corticogenesis. Lack of evidence of clonally related apoptotic cells also indicates that cell death occurs randomly. Therefore, molecular signals from the surrounding microenvironment seem to be necessary for the apoptotic pathway to be turned on, thus determining the fate of post-mitotic cells
Ottimi risultati dal controllo e rilevamento dell’illuminazione e del microclima della sala principale del Museo di Carpineto Romano
No full textA proposal to redact an Atlant of historical materials and structures degradation based on thermovision analysis
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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