Adjuvant chemotherapy for localised resectable soft-tissue sarcoma of adults: meta-analysis of individual data

BACKGROUND: Individually, randomised trials have not shown conclusively whether adjuvant chemotherapy benefits adult patients with localised resectable soft-tissue sarcoma.METHODS: A quantitative meta-analysis of updated data from individual patients from all available randomised trials was carried out to assess whether adjuvant chemotherapy improves overall survival, recurrence-free survival, and local and distant recurrence-free intervals (RFI) and whether chemotherapy is differentially effective in patients defined by age, sex, disease status at randomisation, disease site, histology, grade, tumour size, extent of resection, and use of radiotherapy.FINDINGS: 1568 patients from 14 trials of doxorubicin-based adjuvant chemotherapy were included (median follow-up 9.4 years). Hazard ratios of 0.73 (95% CI 0.56-0.94, p = 0.016) for local RFI, 0.70 (0.57-0.85, p = 0.0003) for distant RFI, and 0.75 (0.64-0.87, p = 0.0001) for overall recurrence-free survival, correspond to absolute benefits from adjuvant chemotherapy of 6% (95% CI 1-10), 10% (5-15), and 10% (5-15), respectively, at 10 years. For overall survival the hazard ratio of 0.89 (0.76-1.03) was not significant (p = 0.12), but represents an absolute benefit of 4% (1-9) at 10 years. These results were not affected by prespecified changes in the groups of patients analysed. There was no consistent evidence that the relative effect of adjuvant chemotherapy differed for any subgroup of patients for any endpoint. However, the best evidence of an effect of adjuvant chemotherapy for survival was seen in patients with sarcomas of the extremities.INTERPRETATION: The meta-analysis provides evidence that adjuvant doxorubicin-based chemotherapy significantly improves the time to local and distant recurrence and overall recurrence-free survival. There is a trend towards improved overall survival.

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

Diabetes in the United Kingdom: Analysis of QRESEARCH Data

Report describing 10 year trends in incidence, prevalence and mortality of diabetes and obesity by age, sex, deprivation and ethnicity from this Primary Care database.

Briefing 24: NCMP data 2008/9, East of England: Data quality

One in a series of six data briefings based on regional-level analysis of data from the National Child Measurement Programme (NCMP) undertaken by the National Obesity Observatory (NOO). The briefings are intended to complement the headline results for the region published in January 2010. This briefing covers issues relating to the quality and completeness of the NCMP data. Detailed analysis of the NCMP at national level is available from NOO at http://www.noo.org.uk/NOO_pubInformation on the methods used to

Capillaria spp. eggs in Patagonian archaeological sites: statistical analysis of morphometric data

Discriminant analysis was used to identify eggs of Capillaria spp. at specific level found in organic remains from an archaeological site in Patagonia, Argentina, dated of 6,540 ± 110 years before present. In order to distinguish eggshell morphology 149 eggs were measured and grouped into four arbitrary subsets. The analysis used on egg width and length discriminated them into different morphotypes (Wilks' lambda = 0.381, p < 0.05). The correlation analysis suggests that width was the most important variable to discriminate among the Capillaria spp. egg morphotypes (Pearson coefficient = 0.950, p < 0.05). The study of eggshell patterns, the relative frequency in the sample, and the morphometric data allowed us to correlate the four morphotypes with Capillaria species.

Application of compositional data analysis to geochemical data of marine sediments

In an earlier investigation (Burger et al., 2000) five sediment cores near the RodriguesTriple Junction in the Indian Ocean were studied applying classical statistical methods(fuzzy c-means clustering, linear mixing model, principal component analysis) for theextraction of endmembers and evaluating the spatial and temporal variation ofgeochemical signals. Three main factors of sedimentation were expected by the marinegeologists: a volcano-genetic, a hydro-hydrothermal and an ultra-basic factor. Thedisplay of fuzzy membership values and/or factor scores versus depth providedconsistent results for two factors only; the ultra-basic component could not beidentified. The reason for this may be that only traditional statistical methods wereapplied, i.e. the untransformed components were used and the cosine-theta coefficient assimilarity measure.During the last decade considerable progress in compositional data analysis was madeand many case studies were published using new tools for exploratory analysis of thesedata. Therefore it makes sense to check if the application of suitable data transformations,reduction of the D-part simplex to two or three factors and visualinterpretation of the factor scores would lead to a revision of earlier results and toanswers to open questions . In this paper we follow the lines of a paper of R. Tolosana-Delgado et al. (2005) starting with a problem-oriented interpretation of the biplotscattergram, extracting compositional factors, ilr-transformation of the components andvisualization of the factor scores in a spatial context: The compositional factors will beplotted versus depth (time) of the core samples in order to facilitate the identification ofthe expected sources of the sedimentary process.Kew words: compositional data analysis, biplot, deep sea sediments

Coherent forecasting of multiple-decrement life tables: a test using Japanese cause of death data

Planners in public and private institutions would like coherent forecasts of the components of age-specic mortality, such as causes of death. This has been di cult toachieve because the relative values of the forecast components often fail to behave ina way that is coherent with historical experience. In addition, when the group forecasts are combined the result is often incompatible with an all-groups forecast. It hasbeen shown that cause-specic mortality forecasts are pessimistic when compared withall-cause forecasts (Wilmoth, 1995). This paper abandons the conventional approachof using log mortality rates and forecasts the density of deaths in the life table. Sincethese values obey a unit sum constraint for both conventional single-decrement life tables (only one absorbing state) and multiple-decrement tables (more than one absorbingstate), they are intrinsically relative rather than absolute values across decrements aswell as ages. Using the methods of Compositional Data Analysis pioneered by Aitchison(1986), death densities are transformed into the real space so that the full range of multivariate statistics can be applied, then back-transformed to positive values so that theunit sum constraint is honoured. The structure of the best-known, single-decrementmortality-rate forecasting model, devised by Lee and Carter (1992), is expressed incompositional form and the results from the two models are compared. The compositional model is extended to a multiple-decrement form and used to forecast mortalityby cause of death for Japan

Exploration of geological variability and possible processes through the use of compositional data analysis: an example using scottish metamorphosed

Developments in the statistical analysis of compositional data over the last twodecades have made possible a much deeper exploration of the nature of variability,and the possible processes associated with compositional data sets from manydisciplines. In this paper we concentrate on geochemical data sets. First we explainhow hypotheses of compositional variability may be formulated within the naturalsample space, the unit simplex, including useful hypotheses of subcompositionaldiscrimination and specific perturbational change. Then we develop through standardmethodology, such as generalised likelihood ratio tests, statistical tools to allow thesystematic investigation of a complete lattice of such hypotheses. Some of these tests are simple adaptations of existing multivariate tests but others require specialconstruction. We comment on the use of graphical methods in compositional dataanalysis and on the ordination of specimens. The recent development of the conceptof compositional processes is then explained together with the necessary tools for astaying- in-the-simplex approach, namely compositional singular value decompositions. All these statistical techniques are illustrated for a substantial compositional data set, consisting of 209 major-oxide and rare-element compositions of metamorphosed limestones from the Northeast and Central Highlands of Scotland.Finally we point out a number of unresolved problems in the statistical analysis ofcompositional processes