Cancer-related therapies at the end of life in hospitalized cancer patients from four Swiss cantons: SAKK 89/09.

The use of cancer-related therapies in cancer patients hospitalized at the end of life has increased in many countries over time. Given the scarcity of published Swiss data, the objective of this study was to evaluate the influence of hospital type and other factors on the delivery of health care during the last month before death. Claims data were used to assess health care utilization of cancer patients (identified by cancer registry data of four participating Swiss cantons) who deceased between 2006 and 2008. Primary endpoints were delivery of cancer-related therapies during the last 30 days before death. Multivariate logistic regression assessed the explanatory value of hospital type, patient and geographic characteristics. Of 3,809 identified cancer patients in the claims database, 2,086 patients dying from cancer were hospitalized during the last 30 days before death, generating 2,262 inpatient episodes. Anticancer drug therapy was given in 22.2% and radiotherapy in 11.7% of episodes. Besides age and cancer type, the canton of residence and hospital type showed independent, statistically significant associations with intensity of care, which was highest in university hospitals. These results should initiate a discussion among oncologists in Switzerland and may question the compliance with standard of care guidelines for terminal cancer patients.

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.

Geographic variation in floral allometry suggests repeated transitions between selfing and outcrossing in a mixed mating plant

• Premise of the study: Isometric and allometric scaling of a conserved floral plan could provide a parsimonious mechanism for rapid and reversible transitions between breeding systems. This scaling may occur during transitions between predominant autogamy and xenogamy, contributing to the maintenance of a stable mixed mating system. • Methods: We compared nine disjunct populations of the polytypic, mixed mating species Oenothera flava (Onagraceae) to two parapatric relatives, the obligately xenogamous species O. acutissima and the mixed mating species O. triloba. We compared floral morphology of all taxa using principal component analysis (PCA) and developmental trajectories of floral organs using ANCOVA homogeneity of slopes. • Key results: The PCA revealed both isometric and allometric scaling of a conserved floral plan. Three principal components (PCs) explained 92.5% of the variation in the three species. PC1 predominantly loaded on measures of floral size and accounts for 36% of the variation. PC2 accounted for 35% of the variation, predominantly in traits that influence pollinator handling. PC3 accounted for 22% of the variation, primarily in anther–stigma distance (herkogamy). During O. flava subsp. taraxacoides development, style elongation was accelerated relative to anthers, resulting in positive herkogamy. During O. flava subsp. flava development, style elongation was decelerated, resulting in zero or negative herkogamy. Of the two populations with intermediate morphology, style elongation was accelerated in one population and decelerated in the other. • Conclusions: Isometric and allometric scaling of floral organs in North American Oenothera section Lavauxia drive variation in breeding system. Multiple developmental paths to intermediate phenotypes support the likelihood of multiple mating system transitions.

A chrysophyte-based quantitative reconstruction of winter severity from varved lake sediments in NE Poland during the past millennium and its relationship to natural climate variability

Chrysophyte cysts are recognized as powerful proxies of cold-season temperatures. In this paper we use the relationship between chrysophyte assemblages and the number of days below 4 °C (DB4 °C) in the epilimnion of a lake in northern Poland to develop a transfer function and to reconstruct winter severity in Poland for the last millennium. DB4 °C is a climate variable related to the length of the winter. Multivariate ordination techniques were used to study the distribution of chrysophytes from sediment traps of 37 low-land lakes distributed along a variety of environmental and climatic gradients in northern Poland. Of all the environmental variables measured, stepwise variable selection and individual Redundancy analyses (RDA) identified DB4 °C as the most important variable for chrysophytes, explaining a portion of variance independent of variables related to water chemistry (conductivity, chlorides, K, sulfates), which were also important. A quantitative transfer function was created to estimate DB4 °C from sedimentary assemblages using partial least square regression (PLS). The two-component model (PLS-2) had a coefficient of determination of View the MathML sourceRcross2 = 0.58, with root mean squared error of prediction (RMSEP, based on leave-one-out) of 3.41 days. The resulting transfer function was applied to an annually-varved sediment core from Lake Żabińskie, providing a new sub-decadal quantitative reconstruction of DB4 °C with high chronological accuracy for the period AD 1000–2010. During Medieval Times (AD 1180–1440) winters were generally shorter (warmer) except for a decade with very long and severe winters around AD 1260–1270 (following the AD 1258 volcanic eruption). The 16th and 17th centuries and the beginning of the 19th century experienced very long severe winters. Comparison with other European cold-season reconstructions and atmospheric indices for this region indicates that large parts of the winter variability (reconstructed DB4 °C) is due to the interplay between the oscillations of the zonal flow controlled by the North Atlantic Oscillation (NAO) and the influence of continental anticyclonic systems (Siberian High, East Atlantic/Western Russia pattern). Differences with other European records are attributed to geographic climatological differences between Poland and Western Europe (Low Countries, Alps). Striking correspondence between the combined volcanic and solar forcing and the DB4 °C reconstruction prior to the 20th century suggests that winter climate in Poland responds mostly to natural forced variability (volcanic and solar) and the influence of unforced variability is low.