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Purpose The purpose of this study was to demonstrate the prognostic significance of changes in body composition in patients with newly diagnosed hepatocellular carcinoma (HCC). Materials and Methods Patients (n=178) newly diagnosed with HCC participated in the study between 2007 and 2012. Areas of skeletal muscle and abdominal fat were directly measured using a threedimensional workstation. Cox proportional-hazards modes were used to estimate the effect of baseline variables on overall survival. The inverse probability of treatment weighting (IPTW) method was used to minimize confounding bias. Results Cutoff values for sarcopenia, obtained from receiver-operating characteristic curves, were defined as skeletal muscle index at the third lumbar vertebra of ≤ 45.8 cm/m² for males and ≤ 43.0 cm/m² for females. Sarcopenia patients were older, more likely to be female, and had lower body mass index. Univariable analysis showed that the presence of sarcopenia and visceral to subcutaneous fat area ratio (VSR) were significantly associated with prognosis. The multivariable analyses revealed that VSR was predictive of overall survival. However, in the multivariable Cox model adjusted by IPTW, sarcopenia, not VSR, were associated with overall survival. Conclusion The presence of sarcopenia at HCC diagnosis is independently associated with survival. [ABSTRACT FROM AUTHOR]

This paper concerns the use of synthetic data for protecting the confidentiality of business data during statistical analysis. Synthetic data sets are traditionally constructed by replacing sensitive values in a confidential data set with draws from statistical models estimated on the confidential data set. Unfortunately, the process of generating effective statistical models can be a difficult and labour-intensive task. Recently, it has been proposed to use easily-implemented methods from machine learning instead of statistical model estimation in the data synthesis task. J. Drechsler and J.P. Reiter (2011) have conducted an evaluation of four such methods, and have found that regression trees could give rise to synthetic data sets which provide reliable analysis results as well as low disclosure risks. Their conclusion was based on simulations using a subset of the 2002 Uganda census public use file. It is an interesting question whether the same conclusion applies to other types of data with different characteristics, for example business data which have quite different characteristics from population census and survey data. In particular, business data generally have few variables that are mostly categorical, and often have highly skewed distributions with outliers. In this paper we investigate the applicability of regression-tree-based methods for constructing synthetic business data. We give a detailed example comparing exploratory data analysis and linear regression results under two variants of a regression-tree-based synthetic data approach. We also include an evaluation of the analysis results with respect to the results of analysis of the original data. We further investigate the impact of different stopping criteria on performance. While it is certainly true that any method designed to protect confidentiality introduces error, and may indeed give misleading conclusions, our analysis of the results for synthesisers based on CART models has provided some evidence that this error is not random but is due to the particular characteristics of business data. We conclude that more careful analysis needs to be done in applying these methods and end users certainly need aware of possible discrepancies.

Chiral dimeric tridentate NHC-amidate-alkoxide palladium(II) complexes, 3a and 3b, effected oxidative boron Heck-type reactions of aryl boronic acids with both acyclic and cyclic alkenes at room temperature to afford the corresponding coupling products with high enantioselectivities. The high degree of enantioselection, far superior to existing methods, stems from differences in the nonbonding interactions in the proposed transition states, due to the influence from bulky substituents of the alkene substrates and the "counter axial groups" of the palladium(II) catalysts. [ABSTRACT FROM AUTHOR]