1,721,105 research outputs found
EXPLORATORY AND CONFIRMATORY FACTOR ANALYSIS TO IDENTIFY AND VALIDATE DIETARY PATTERNS: AN APPLICATION TO A CASE-CONTROL STUDY OF GASTRIC CANCER.
No full textIn nutritional epidemiology, the use of dietary pattern methods, based on foods or nutrients, has increased substantially over the past several years. Use of explorative statistical methods is one way to examine dietary patterns in populations. Of these, exploratory factor analysis (EFA) is a data aggregation procedure used to reduce dietary data into meaningful food or nutrient patterns based on inter-correlations between dietary items. The factors are then named, usually according to those foods or nutrients that most heavily contribute to the pattern, and the patterns can then be used as the primary exposure variables in dietary studies. Several studies have used explorative methods to identify dietary patterns in epidemiological studies, but few have validated the factors with confirmatory analyses. The purpose of my PhD thesis is to further knowledge of factor analysis methods in nutritional epidemiologic research. In particular, I studied the application of the confirmatory factor analysis (CFA) to validate nutrient-dietary patterns derived from EFA. The major difference between these two variants of factor analyses is that: in EFA all nutrients load on all factors (a posteriori approach), while in CFA only the nutrients decided on a priori are included. One of the criteria used for the a priori decision, could be the magnitude of the nutrient’s loading in an EFA. CFA is a type of structural equation modeling that deals specifically with measurement models, that is, the relationships between measured variables and latent variables (i.e., a hypothetical construct that is not directly measured or observed in the study). Therefore, this statistical technique allows the researchers to test and verify a particular model or factor structure that they believe underlies the variables measured in the study. In this work, the measured variables are represented by the nutrients and the latent variables are represented by the dietary patterns derived from an EFA. The acceptability of the tested CFA models is usually evaluated by descriptive goodness-of-fit indices. Among these indices, comparative fit index (CFI), the non-normed fit indices (NNFI) are the most used. By convention, CFI and NNFI ≥0.90 indicate an acceptable fit. The fit of the model is also judged by the root mean square residual (RMR) and the root mean square error of approximation (RMSEA). By convention, RMR and RMSEA values close to 0 indicate a good fit. To assess the fit of a CFA model, the chi-square test was also used. This test has as null hypothesis that the model fits the data. However, with large samples and real-world data, the chi-square statistic is very frequently significant even if the model provides a good fit. For these reasons, the mentioned indices and the chi-square test must be considered together, and it is not frequent conclude that a CFA model fits the data even if the chi-square p–value is significant.
In my PhD project, I applied EFA analyses to derive nutrient-dietary patterns, based on a set of 28 selected micro- and macro-nutrients, in the context of a case-control study of gastric cancer. To decide how many factors to extract, I carried out and compared different CFA models that tested structures from 2 to 6 latent factors derived from EFA, in which I included only those nutrients with explored factor loadings ≥0.63. In CFA models, the included nutrient items were allowed to load on only one factor, and loadings were fixed at zero for the other factors. Since the latent factors in CFA models were derived from orthogonal EFA solutions, I fixed to zero the factors’ covariance. Then, to improve the parsimony and interpretability of CFA solutions, I also tested revised models, i.e. factor covariances were freed to estimate the relationship between the latent dimensions, and different cut-off, other than 0.63, were also considered.
Using the EFA, the cumulative percentages of variance explained by six-, five-, four-, three-, and two-factor solutions were approximately equal to 84%, 80%, 75%, 69% and 63%, respectively. I excluded from CFA models the six-factor solution, since it showed a pattern based only on a single nutrient. Throughout solutions from five- to two-factor, all confirmed factor loadings ranged from 0.5 to 1. The associated t tests (greater than 3.291 with p<0.001) indicated that the loading of each nutrient was significantly different from zero. The chi-square test gives p-values highly significant for each CFA model, that lead to reject that the models fit the data. However, because of the problems with this significance test, this findings by itself did not cause to reject the models. Throughout the different CFA models with factor covariance free to estimate, the RMR values were around the 0.1 threshold for an acceptable fit. The RMSEA values were somewhat higher than the threshold for an acceptable fit. Considering the CFA models with factor covariance fixed to zero, the CFI values increased with the number of retained factors, from 0.57 for the five-factor model to 0.76 for the two-factor model including nutrients with explored factor loadings ≥0.70. The CFI values for the CFA models with factor covariance free to estimate were higher compared to those with factor covariance fixed to zero, to reach 0.80 for the two-factor model including nutrients with loadings ≥0.70, quite close to the 0.90 threshold for an acceptable fit. The NFI values were very similar than those of the CFI, whereas the NNFI values were lower.
In conclusion, results from all CFA models are not very satisfactory. For this reason, in order to better understand the performance of this statistical technique, I tested and compared results from CFA applied on simulated datasets characterized by a structure generated “ad hoc”, such as each variable was highly correlated only to one factor, for a total of four orthogonal factors. In this case, I verified that CFA technique provides satisfactory results, in particular when the sample size is at least 500, although limitations regarding some goodness-of-fit indices remain.
Moreover, a different use of the CFA could be particularly useful. For example if the confirmed factors were tested in a different study as true a priori factors: the factors identified in one group could be applied in a different group using CFA based on the same nutrients to compute scores. Hence, the factor scores could be acceptable and robust as markers of nutrient intake pattern on group levels and may prove useful in studies of diet–disease relationships. Nevertheless, until factor analysis gains more experience in nutrition, it will be difficult to define valid criteria for a good fit in this discipline and methodologies for improving fit
The application of factor analysis in the identification of dietary patterns : some notes from an analysis of gastric cancer
No full textAndamento della natimortalità, della mortalità perinatale ed infantile nelle diverse regioni italiane : 1990-2002
No full textNegli ultimi 15 anni la realtà sociale italiana è marcatamente cambiata, sia con riferimento ad una maggiore diffusione dell'assistenza ostetrica in tutte le regioni, sia al fenomeno migratorio. Abbiamo analizzato i dati relativi al numero di nati vivi e nati morti in Italia, entro il primo anno di vita. Il tasso di natimortalità ha mostrato temporanei aumenti nel corso degli anni, tuttavia ha teso complessivamente alla diminuzione, passando da 5,42 nati morti per 1000 nati nel 1990 a 3,15 nel 2002, una riduzione pari al 42%.
Con riferimento alla mortalità perinatale, nel periodo 1990-2002 è diminuita da 10,46 a 5,44/1000 nati. Nel 1990 il tasso il tasso di mortalità infantile in Italia era pari a 8,18 morti nel primo anno di vita su 1000 nati vivi, il valore corrispondente nel 2002 era pari a 4,36 su 1000 nati vivi. I dati aggregati per ampie aree geografiche mostrano una diminuzione costante in tutte le aree, dove le differenze tra i tassi vanno riducendosi, pur permanendo una differenza tra Nord e Centro, Sud e Isole.In this paper we have analysed the temporal trend of stillbirth rates and perinatal and infant mortality rates during the period 1990-2002 in the various Italian regions, using data of the Health for All software provided in Italy by the national Institute for Health. The still birth rates declined from 542 stillbirth/1000 birth in the 1990 to 3.15/1000 birth in the 2002 with a reduction equal to 42%. Considering the perinatal mortality rates, the rate was 10.46/1000 births during the 1990 and 5.44/1000 births during the 2002. Otherwise, the infant mortality rate was 8.18/1000 live births during the 1990 and 4.36/1000 live births during the 2002. Considering the rates in the various Italian regions we observed that the rates were generally higher in the south areas of the country and in the island, but in general we observed a reduction of the differencies among the various Italian regions during the considered period
European trends in breast cancer mortality, 1980 e 2017 and predictions to 2025
Full text linkBackground: Breast cancer mortality in European women has been falling for three decades. We analysed trends in mortality from breast cancer in Europe over the period 1980 e 2017 and predicted number of deaths and rates to 2025.
Methods: We extracted death certification data for breast cancer in women for 35 European countries, between 1980 and 2017, from the World Health Organisation database. We computed the age-standardised (world standard population) mortality rates per 100,000 person-years, by country and calendar year. We obtained also predictions for 2025 using a joinpoint regression model and calculated the number of avoided deaths over the period 1994e2025.
Results: The mortality rate declined from 15.0 in 2012 to 14.4 in 2017 per 100,000 women (3.9%) for the European Union (EU)-27. This fall was greater in the EU-14 (5.2%), whereas rates rose in the transitional countries during this period by 1.9%. Mortality rate predictions across Europe are expected to reach relatively uniform levels in 2025. During the studied period, favourable trends in mortality emerged in most countries, with the greatest decrease in Denmark, whereas Poland and Romania showed an upward trend. The largest predicted decrease in breast cancer mortality was estimated for the United Kingdom (12.2/100,000 women in 2025), leading to the estimated avoidance of 150,000 breast cancer deaths over the period 1994e2025 and 470,000 in the EU-27
Age period cohort analysis of cancer mortality data: methods and application to italian male mortality data for gastric cancer and cancers of the oral cavity and pharynx
No full textObjectives
The paper’s objective is to provide an in depth age period cohort analysis of gastric and oral cancers in Italian men.
Methods
Mortality data from gastric cancer and oral cavity and pharynx cancer in Italian men was obtained from the WHO mortality database for the period 1950-2003. An in depth age period cohort analysis was performed on the data.
Results
Analysis of gastric cancer mortality showed descending trends for both period and cohort effects. For oral cancer, period effects rose steeply until the late 1980’s to then descend to the last period; cohorts had a downward trend up to 1910 to then rise up the 1960’s, displaying a descent for the effects of the last two birth cohorts.
Conclusions
The descending effects of period and cohort from gastric cancer in men reflect the effect of the improving quality of life in Italy and predict further falls in mortality.
Period effects in oral cancer mortality reflect the influence of smoking cessation in Italian men with a downward trend for most recent years. The cohort effects are probably influenced by alcohol consumption, which has longer lasting consequences, and only show a favourable pattern for the most recent cohorts
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
- …
