Obesity Surgery

, Volume 19, Issue 6, pp 732–737 | Cite as

Analysis of Weight Loss After Bariatric Surgery Using Mixed-Effects Linear Modeling

  • Ramsey M. Dallal
  • Brian B. Quebbemann
  • Lacy H. Hunt
  • Leonard E. Braitman
Research Article



The standard analysis of bariatric surgery weight outcomes data (using t tests) is well known. However, these uncontrolled comparisons may yield misleading results and limit the range of research questions. The aim of the study was to develop a valid approach to the longitudinal analysis of weight loss outcomes after bariatric surgery using multivariable mixed models. This study has a multi-institutional setting.


We developed a mixed-effects model to examine weight after gastric bypass surgery while controlling for several independent variables: gender, anastomotic technique, age, race, initial weight, height, and institution. We contrasted this approach with traditional uncontrolled analyses using percent excess weight loss (%EWL).


One thousand one hundred sixty-eight gastric bypass procedures were performed between 2000 and 2006. The average %EWL at 1, 2, and 3 years was 71%, 79%, and 76%, respectively. Using weight as the outcome variable, initial weight and gender were the only independent predictors of outcome (p < 0.001). %EWL was substantially less accurate than weight as an outcome measure in multivariable modeling. Including initial weight and height as separate independent variables yielded a more accurate model than using initial body mass index. In a traditional uncontrolled analysis, average %EWL was higher in women than men. However, average weight loss was lower, not higher, in women (p < 0.001) in our multivariable mixed model. Height, surgical technique, race and age did not independently predict weight loss.


Multivariable mixed models provide more accurate analyses of weight loss surgery than traditional methods and should be used in studies that examine repeated measurements.


Gastric bypass Bariatric surgery Mixed models 


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Copyright information

© Springer Science + Business Media, LLC 2009

Authors and Affiliations

  • Ramsey M. Dallal
    • 1
    • 4
  • Brian B. Quebbemann
    • 2
  • Lacy H. Hunt
    • 3
  • Leonard E. Braitman
    • 3
  1. 1.Department of SurgeryAlbert Einstein Healthcare NetworkPhiladelphiaUSA
  2. 2.The N.E.W. ProgramNewport BeachUSA
  3. 3.Office for Research and Technology DevelopmentAlbert Einstein Healthcare NetworkPhiladelphiaUSA
  4. 4.Department of MIS/Bariatric SurgeryAlbert Einstein Healthcare NetworkPhiladelphiaUSA

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