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Background: Laparoscopic surgery for rectal cancer is widely performed all over the world and several randomized controlled trials have been reported. However, the usefulness of laparoscopic surgery compared with open surgery has not been demonstrated sufficiently, especially for the low rectal area. Objective: The aim of this study was to investigate the hypothesis that laparoscopic primary tumor resection is safe and effective when compared with the open approach for locally advanced low rectal cancer. Patients and Methods: Data from patients with clinical stage II to III low rectal cancer below the peritoneal reflection were collected and analyzed. The operations were performed from 2010 to 2011. Short-term outcomes and long-term prognosis were analyzed with propensity score matching. Results: Of 1608 cases collated from69 institutes, 1500 cases were eligible for analysis.The caseswerematched into 482 laparoscopic and 482 open cases. The mean height of the tumor fromthe anal vergewas 4.6cm. Preoperative treatment was performed in 35%of the patients. The conversion rate fromlaparoscopic to open surgery was 5.2%. Estimated blood loss during laparoscopic surgery was significantly less than that during open surgery (90 vs 625mL, P < 0.001). Overall, the occurrence of complications after laparoscopic surgeries was less than that after open surgeries (30.3% vs 39.2%, P = 0.005). Three-year overall survival rates were 89.9% [95% confidence interval (95% CI) 86.7-92.4] and 90.4% (95% CI 87.4-92.8) in the laparoscopic and open groups, respectively, and no significant difference was seen between the 2 groups. No significant differencewas observed in recurrence-free survival (RFS) between the 2 groups (3-year RFS: 70.9%, 68.4 to 74.2 vs 71.8%, 67.5 to 75.7). Conclusion: Laparoscopic surgery could be considered as a treatment option for advanced, low rectal cancer below the peritoneal reflection, based on the short-term and long-term results of this large cohort study (UMIN-ID: UMIN000013919). [ABSTRACT FROM AUTHOR]

Background: To confirm the effectiveness of sivelestat, a clinical trial was conducted comparing sivelestat with conventional treatment in an open, nonrandomized, multicenter study of patients with systemic inflammatory response syndrome (SIRS)- associated acute lung injury. The primary endpoint was ventilator-free days (VFD). Methods: This study adopted a "cluster entry" method to control for patient selection bias arising from the unblinded and nonrandomized clinical trial. Thus, all patients in the same hospital during the same entry period entered the same treatment arm, and entry periods did not overlap. In the primary analysis of VFD, adjusted mean VFD values were compared between groups using the inverse probability of treatment weighted (IPTW) method, based on propensity score, for control of confounding factors. Results: There were 374 patients in the sivelestat group and 168 in the conventional therapy group. The primary analysis confirmed that sivelestat was effective (between-group difference of adjusted mean was 3.5 [2-sided 95% confidence interval, 1.3-5.8]; P ¼ .0022). Conclusions: In general, a study where all patients in the same cluster enter the same treatment arm has within-cluster correlations, which need to be considered in the study analysis. However, in analysis using the IPTW method, it is usual to use a robust variance estimator, the sandwich variance estimator, which is consistent regardless of whether the specification of within-cluster correlation structure is correct. Thus, in the analysis using the IPTW method, it was found that it was not necessary to adopt any other adjustment method for withincluster correlation. [ABSTRACT FROM AUTHOR]

We propose a novel approach to finding an optimal subspace of multi-dimensional variables for identifying a cluster structure of objects. When some variables are irrelevant to the cluster structure and are correlated between themselves, they are likely to have an adverse effect on clustering of objects. In such situations, the proposed method aims to obtain an optimal subspace for partitioning objects by eliminating the effects of these irrelevant variables. The proposed method can be considered an extension of reduced k-means analysis and factorial k-means analysis for the settings where irrelevant variables are correlated. The proposed method is applied for the analyses of artificial and real data to investigate how it performs as compared to the existing methods. [ABSTRACT FROM AUTHOR]