title: Estimating utilities for chronic kidney disease, using SF-36 and SF-12-based measures: challenges in a population of veterans with diabetes.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Center for Healthcare Knowledge Management, Veterans Health Administration New Jersey, East Orange, NJ, USA.
authors.forename: Mangala
authors.initials: M
authors.lastname: Rajan
authors.forename: Kuan-Chi
authors.initials: KC
authors.lastname: Lai
authors.forename: Chin-Lin
authors.initials: CL
authors.lastname: Tseng
authors.forename: Shirley
authors.initials: S
authors.lastname: Qian
authors.forename: Alfredo
authors.initials: A
authors.lastname: Selim
authors.forename: Lewis
authors.initials: L
authors.lastname: Kazis
authors.forename: Leonard
authors.initials: L
authors.lastname: Pogach
authors.forename: Anushua
authors.initials: A
authors.lastname: Sinha
mesh_terms: D000328:Adult; D000368:Aged; D003362:Cost-Benefit Analysis; D003430:Cross-Sectional Studies; D003920:Diabetes Mellitus / Q000523:psychology* / Q000628:therapy; D005260:Female; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011569:Psychiatric Status Rating Scales; D011594:Psychometrics / Q000295:instrumentation; D011788:Quality of Life / Q000523:psychology*; D012044:Regression Analysis; D006435:Renal Dialysis; D051436:Renal Insufficiency, Chronic / Q000503:physiopathology / Q000523:psychology*; D012720:Severity of Illness Index; D018434:Sickness Impact Profile; D011795:Surveys and Questionnaires; D014728:Veterans / Q000523:psychology*