Prognostic value of histopathological regression in 850 neoadjuvantly treated oesophagogastric adenocarcinomas.

BACKGROUND Recently, histopathological tumour regression, prevalence of signet ring cells, and localisation were reported as prognostic factors in neoadjuvantly treated oesophagogastric (junctional and gastric) cancer. This exploratory retrospective study analyses independent prognostic factors within a large patient cohort after preoperative chemotherapy including clinical and histopathological factors. METHODS In all, 850 patients presenting with oesophagogastric cancer staged cT3/4 Nany cM0/x were treated with neoadjuvant chemotherapy followed by resection in two academic centres. Patient data were documented in a prospective database and retrospectively analysed. RESULTS Of all factors prognostic on univariate analysis, only clinical response, complications, ypTNM stage, and R category were independently prognostic (P<0.01) on multivariate analysis. Tumour localisation and signet ring cells were independently prognostic only when investigator-dependent clinical response evaluation was excluded from the multivariate model. Histopathological tumour regression correlates with tumour grading, Laurén classification, clinical response, ypT, ypN, and R categories but was not identified as an independent prognostic factor. Within R0-resected patients only surgical complications and ypTNM stage were independent prognostic factors. CONCLUSIONS Only established prognostic factors like ypTNM stage, R category, and complications were identified as independent prognostic factors in resected patients after neoadjuvant chemotherapy. In contrast, histopathological tumour regression was not found as an independent prognostic marker.

Objective consensus from decision trees

BackgroundConsensus-based approaches provide an alternative to evidence-based decision making, especially in situations where high-level evidence is limited. Our aim was to demonstrate a novel source of information, objective consensus based on recommendations in decision tree format from multiple sources.MethodsBased on nine sample recommendations in decision tree format a representative analysis was performed. The most common (mode) recommendations for each eventuality (each permutation of parameters) were determined. The same procedure was applied to real clinical recommendations for primary radiotherapy for prostate cancer. Data was collected from 16 radiation oncology centres, converted into decision tree format and analyzed in order to determine the objective consensus.ResultsBased on information from multiple sources in decision tree format, treatment recommendations can be assessed for every parameter combination. An objective consensus can be determined by means of mode recommendations without compromise or confrontation among the parties. In the clinical example involving prostate cancer therapy, three parameters were used with two cut-off values each (Gleason score, PSA, T-stage) resulting in a total of 27 possible combinations per decision tree. Despite significant variations among the recommendations, a mode recommendation could be found for specific combinations of parameters.ConclusionRecommendations represented as decision trees can serve as a basis for objective consensus among multiple parties.

Plotting regression coefficients and other estimates

Graphical display of regression results has become increasingly popular in presentations and in scientific literature because graphs are often much easier to read than tables. Such plots can be produced in Stata by the marginsplot command (see [R] marginsplot). However, while marginsplot is versatile and flexible, it has two major limitations: it can only process results left behind by margins (see [R] margins), and it can handle only one set of results at a time. In this article, I introduce a new command called coefplot that overcomes these limitations. It plots results from any estimation command and combines results from several models into one graph. The default behavior of coefplot is to plot markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce other types of graphs. I illustrate the capabilities of coefplot by using a series of examples.

Effects of elevation and land use on the biomass of trees, shrubs and herbs at Mount Kilimanjaro

The protection and sustainable management of forest carbon stocks, particularly in the tropics, is a key factor in the mitigation of global change effects. However, our knowledge of how land use and elevation affect carbon stocks in tropical ecosystems is very limited. We compared aboveground biomass of trees, shrubs and herbs for eleven natural and human-influenced habitat types occurring over a wide elevation gradient (866–4550 m) at the world's highest solitary mountain, Mount Kilimanjaro. Thanks to the enormous elevation gradient, we covered important natural habitat types, e.g., savanna woodlands, montane rainforest and afro-alpine vegetation, as well as important land-use types such as maize fields, grasslands, traditional home gardens, coffee plantations and selectively logged forest. To assess tree and shrub biomass with pantropical allometric equations, we measured tree height, diameter at breast height and wood density and to assess herbaceous biomass, we sampled destructively. Among natural habitats, tree biomass was highest at intermediate elevation in the montane zone (340 Mg ha−1), shrub biomass declined linearly from 7 Mg ha−1 at 900 m to zero above 4000 m, and, inverse to tree biomass, herbaceous biomass was lower at mid-elevations (1 Mg ha−1) than in savannas (900 m, 3 Mg ha−1) or alpine vegetation (above 4000 m, 6 Mg ha−1). While the various land-use types dramatically decreased woody biomass at all elevations, though to various degrees, herbaceous biomass was typically increased. Our study highlights tropical montane forest biomass as important aboveground carbon stock and quantifies the extent of the strong aboveground biomass reductions by the major land-use types, common to East Africa. Further, it shows that elevation and land use differently affect different vegetation strata, and thus the matrix for other organisms.

The Blinder-Oaxaca decomposition for linear regression models

The counterfactual decomposition technique popularized by Blinder (1973, Journal of Human Resources, 436–455) and Oaxaca (1973, International Economic Review, 693–709) is widely used to study mean outcome differences between groups. For example, the technique is often used to analyze wage gaps by sex or race. This article summarizes the technique and addresses several complications, such as the identification of effects of categorical predictors in the detailed decomposition or the estimation of standard errors. A new command called oaxaca is introduced, and examples illustrating its usage are given.

Making regression tables simplified

estout, introduced by Jann (Stata Journal 5: 288–308), is a useful tool for producing regression tables from stored estimates. However, its syntax is relatively complex and commands may turn out long even for simple tables. Furthermore, having to store the estimates beforehand can be cumbersome. To facilitate the production of regression tables, I therefore present here two new commands called eststo and esttab. eststo is a wrapper for offcial Stata’s estimates store and simplifies the storing of estimation results for tabulation. esttab, on the other hand, is a wrapper for estout and simplifies compiling nice-looking tables from the stored estimates without much typing. I also provide updates to estout and estadd.

Making regression tables from stored estimates

Organizing and archiving statistical results and processing a subset of those results for publication are important and often underestimated issues in conducting statistical analyses. Because automation of these tasks is often poor, processing results produced by statistical packages is quite laborious and vulnerable to error. I will therefore present a new package called estout that facilitates and automates some of these tasks. This new command can be used to produce regression tables for use with spreadsheets, LaTeX, HTML, or word processors. For example, the results for multiple models can be organized in spreadsheets and can thus be archived in an orderly manner. Alternatively, the results can be directly saved as a publication-ready table for inclusion in, for example, a LaTeX document. estout is implemented as a wrapper for estimates table but has many additional features, such as support for mfx. However, despite its flexibility, estout is—I believe—still very straightforward and easy to use. Furthermore, estout can be customized via so-called defaults files. A tool to make available supplementary statistics called estadd is also provided.

3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

ROBREG: Stata module providing robust regression estimators

robreg provides a number of robust estimators for linear regression models. Among them are the high breakdown-point and high efficiency MM-estimator, the Huber and bisquare M-estimator, and the S-estimator, each supporting classic or robust standard errors. Furthermore, basic versions of the LMS/LQS (least median of squares) and LTS (least trimmed squares) estimators are provided. Note that the moremata package, also available from SSC, is required.

Dynamics of Traditional Ecological Knowledge, Local Use and Conservation of Native Trees and Shrubs in the Bolivian Andes

Native trees and shrubs are essential components of rural landscapes in the semi-arid inner-Andean valleys of Bolivia. They can be found as hedges and bushes in various agroecosystems such as terrace walls, slopes, field boundaries and fallow land. Their distribution and floristic composition are the result of dynamic spatial and temporal interactions between local farmers and the environment. Local uses of natural resources and biodiversity reflect the constantly evolving Andean culture, which can be generally characterised as an intertwining of the human, natural, and spiritual worlds. The aim of the present ethnobotanical study was to analyse the dynamics of traditional ecological knowledge, to ascertain local farmers’ perceptions and uses of native woody species in Andean communities and to associate the results with local conservation activities for the trees and shrubs concerned. Our case study was carried out within two communities of the Tunari National Park (Dept. Cochabamba) in Bolivia. For data collection, research methods from social science (semi-structured interviews, participative observation, participatory mapping) as well as vegetation surveys were combined. Local actors included women and men of all ages as well as families from different social categories and altitudinal levels of permanent residence. Our study indicates that, due to a multitude of socio-economic pressures (e.g. migration of young people) as well as changes in use of biodiversity (e.g. replacement of native by exotic introduced species), the traditional ecological knowledge base of native trees and shrubs and their respective uses has become diminished over time. In many cases it has led to a decline in people’s awareness of native species and as a consequence their practical, emotional and spiritual relationships with them have been lost. However, results also show that applied traditional ecological knowledge has led to local conservation strategies, which have succeeded in protecting those tree and shrub species which are most widely regarded for their multifunctional, constant and exclusive uses (e.g. Schinus molle, Prosopis laevigata, Baccharis dracunculifolia). The presentation will discuss the question if and how applied traditional ecological knowledge positively contributes to local initiatives of sustainable use and conservation of biodiversity in rural areas.

Are carbohydrate storage strategies of trees traceable by early–latewood carbon isotope differences?

Investigations of stable isotopes in early and latewood cellulose offer interesting insights to climate-driven adaptations of trees’ carbon storage strategy during different phenological phases.