icon-folder.gif   Conference Reports for NATAP  
 
  66th Annual Meeting of the
American Association for the
Study of Liver Diseases
Boston, MA Nov 13-17 2015
Back grey_arrow_rt.gif
 
 
 
Treatment Outcomes With 8, 12 and 24 Week Regimens of Ledipasvir/Sofosbuvir for the Treatment of Hepatitis C Infection: Analysis of a Multicenter Prospective, Observational Study
 
 
  Reported by Jules Levin
AASLD 2015 Nov 13-17 San Francisco
 
N Terrault, S Zeuzem, AM Di Bisceglie, JK Lim, PJ Pockros, LM Frazier, A Kuo, AS Lok, ML Shiffman, Z Ben Ari, T Stewart, MS Sulkowski, MW Fried, and DR Nelson for the HCV-TARGET Study Group

AASLD1.gif

AASLD2.gif

AASLD3.gif

AASLD4.gif

AASLD5.gif

AASLD6.gif

AASLD7.gif

AASLD8.gif

AASLD9.gif

AASLD10.gif

AASLD11.gif

AASLD12.gif

HCVNewad.gif

AASLD13.gif

Because the treatment assignment was not random, selection bias was addressed through applying inverse probability weighting (IPW) for creating pseudo-randomization.
 
The IPW creates a "pseudo randomized population" in which each potential baseline predictor of treatment becomes balanced among the two groups. The underlying idea of this approach is to assign a weight to each patient so that all patient characteristics for treatment-specific groups become similar.
 
There are too many predictors in the models above, some of them are collinear. LASSO (Least Absolute Shrinkage and Selection Operator) - a regression method that involves penalizing the absolute size of the regression coefficients - was used to identify candidate predictors of SVR12.
 
The LASSO analysis penalizes the parameter estimates by constraining the sum of the absolute values of the estimates. This method is excellent with trying to decide what variables to include in a multivariable model if you believe the variables to be highly correlated.

AASLD14.gif

AASLD15.gif

AASLD16.gif

AASLD17.gif