Estimating utilities for chronic kidney disease, using SF-36 and SF-12-based measures: challenges in a population of veterans with diabetes

Using transformations of existing quality-of-life data to estimate utilities has the potential to efficiently provide investigators
with utility information. We used within-method and across-method comparisons and estimated disutilities associated with increasing
chronic kidney disease (CKD) severity.

In an observational cohort of veterans with diabetes (DM) and pre-existing SF-36/SF-12 responses, we used six transformation
methods (SF-12 to EQ-5D, SF-36 to HUI2, SF-12 to SF-6D, SF-36 to SF-6D, SF-36 to SF-6D (Bayesian method), and SF-12 to VR-6D)
to estimate unadjusted utilities. CKD severity was staged using glomerular filtration rate estimated from serum creatinines,
with the modification of diet in renal disease formula. We then used multivariate regression to estimate disutilities specifically
associated with CKD severity stage.

Of 67,963 patients, 22,273 patients had recent-onset DM and 45,690 patients had prevalent DM. For the recent-onset group,
the adjusted disutility associated with CKD derived from the six transformation methods ranged from 0.0029 to 0.0045 for stage
2; −0.004 to −0.0009 for early stage 3; −0.017 to −0.010 for late stage 3; −0.023 to −0.012 for stage 4; −0.078 to −0.033
for stage 5; and −0.012 to −0.001 for ESRD/dialysis.

Disutility did not increase monotonically as CKD severity increased. Differences in disutilities estimated using the six different
methods were found. Both findings have implications for using such estimates in economic analyses.