Safety and effectiveness of bariatric surgery: Roux-en-y gastric bypass is superior to gastric banding in the management of morbidly obese patients: a reply to the response by Bhoyrul et al

ABSTRACT: BACKGROUND: We have read the letter by Bhoyrul et al. in response to our recently published article "Safety and effectiveness of bariatric surgery: Roux-en-Y gastric bypass is superior to gastric banding in the management of morbidly obese patients". We strongly disagree with the content of the letter. RESULTS AND DISCUSSION: Bhoyrul et al. base their letter mostly on low level evidence such as single-institutional case series (level IV evidence) and expert opinion (level V evidence). Surprisingly, they do not comment on the randomized controlled trial, which clearly favours gastric bypass over gastric banding. CONCLUSION: The letter by Bhoyrul et al. is based on low level evidence and is itself biased, unsubstantiated, and not supported by the current literature.

A Combinatorial Approach to the Variable Selection in Multiple Linear Regression: Analysis of Selwood et al Data set -A Case Study.

A combinatorial protocol (CP) is introduced here to interface it with the multiple linear regression (MLR) for variable selection. The efficiency of CP-MLR is primarily based on the restriction of entry of correlated variables to the model development stage. It has been used for the analysis of Selwood et al data set [16], and the obtained models are compared with those reported from GFA [8] and MUSEUM [9] approaches. For this data set CP-MLR could identify three highly independent models (27, 28 and 31) with Q2 value in the range of 0.632-0.518. Also, these models are divergent and unique. Even though, the present study does not share any models with GFA [8], and MUSEUM [9] results, there are several descriptors common to all these studies, including the present one. Also a simulation is carried out on the same data set to explain the model formation in CP-MLR. The results demonstrate that the proposed method should be able to offer solutions to data sets with 50 to 60 descriptors in reasonable time frame. By carefully selecting the inter-parameter correlation cutoff values in CP-MLR one can identify divergent models and handle data sets larger than the present one without involving excessive computer time.