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. Author manuscript; available in PMC: 2017 Apr 1.
Published in final edited form as: Clin Gastroenterol Hepatol. 2015 Jun 29;14(4):624–632.e2. doi: 10.1016/j.cgh.2015.06.021

Table 3.

Univariate and Multivariate Linear Regression Models to Predict log(mGFR) in 103 Subjects with Cirrhosis

Univariate Model that Includes only
the log of the GFR Marker		 Multivariate Model that Includes
log(creatinine) and log(cystatin C) in
addition to the GFR Marker1
GFR Marker R-Square for GFR
Marker		 P Value for GFR
Marker		 Partial R-Square
for GFR Marker2 		P Value for the
GFR Marker
log(creatinine) 0.573 <0.0001 0.119 <0.0001
log(cystatin C) 0.508 <0.0001 0.054 0.0002
log(beta-trace protein) 0.472 <0.0001 0.008 0.154
log(beta-2 microglobulin) 0.516 <0.0001 0.008 0.137
log(SDMA) 0.340 <0.0001 0.000 0.782
log(ADMA) 0.080 0.004 0.000 0.974
log(SDMA+ADMA) 0.306 <0.0001 0.000 0.816
log(L-arginine) 0.000 0.985 0.002 0.471
log(L-arginine/SDMA) 0.176 <0.0001 0.002 0.464
log(L-arginine/ADMA) 0.035 0.057 0.002 0.486
log[ L-arginine/(SDMA+ADMA) ] 0.126 0.0002 0.002 0.437
1

For the model that include log(creatinine), log(cystatin C), and no other additional GFR marker but the variables including age, female and African-American, R-Square=0.675, P value for creatinine=<0.0001, P value for cystatin C=0.009

2

Partial R-squares are interpretable as the additional proportion of variance explained by adding the GFR marker to a model that already includes creatinine and cystatin C