A view from the bridge: agreement between the SF-6D utility algorithm and the Health Utilities Index.
BACKGROUND
The SF-6D is a new health state classification and utility scoring system based on 6 dimensions ('6D') of the Short Form 36, and permits a "bridging" transformation between SF-36 responses and utilities. The Health Utilities Index, mark 3 (HUI3) is a valid and reliable multi-attribute health utility scale that is widely used. We assessed within-subject agreement between SF-6D utilities and those from HUI3.


METHODS
Patients at increased risk of sudden cardiac death and participating in a randomized trial of implantable defibrillator therapy completed both instruments at baseline. Score distributions were inspected by scatterplot and histogram and mean score differences compared by paired t-test. Pearson correlation was computed between instrument scores and also between dimension scores within instruments. Between-instrument agreement was by intra-class correlation coefficient (ICC).


RESULTS
SF-6D and HUI3 forms were available from 246 patients. Mean scores for HUI3 and SF-6D were 0.61 (95% CI 0.60-0.63) and 0.58 (95% CI 0.54-0.62) respectively; a difference of 0.03 (p<0.03). Score intervals for HUI3 and SF-6D were (-0.21 to 1.0) and (0.30-0.95). Correlation between the instrument scores was 0.58 (95% CI 0.48-0.68) and agreement by ICC was 0.42 (95% CI 0.31-0.52). Correlations between dimensions of SF-6D were higher than for HUI3.


CONCLUSIONS
Our study casts doubt on the whether utilities and QALYs estimated via SF-6D are comparable with those from HUI3. Utility differences may be due to differences in underlying concepts of health being measured, or different measurement approaches, or both. No gold standard exists for utility measurement and the SF-6D is a valuable addition that permits SF-36 data to be transformed into utilities to estimate QALYs. The challenge is developing a better understanding as to why these classification-based utility instruments differ so markedly in their distributions and point estimates of derived utilities.
Department of Clinical Epidemiology and Biostatistics, McMaster University, Canada. obrienb@mcmaster.ca
Bernie J
BJ
O'Brien
Marian
M
Spath
Gordon
G
Blackhouse
J L
JL
Severens
Paul
P
Dorian
John
J
Brazier
D000465:Algorithms; D003362:Cost-Benefit Analysis; D016757:Death, Sudden, Cardiac / Q000517:prevention & control; D017147:Defibrillators, Implantable / Q000191:economics*; D005060:Europe; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000191:economics / Q000503:physiopathology / Q000628:therapy*; D011788:Quality of Life; D019057:Quality-Adjusted Life Years; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis; D018434:Sickness Impact Profile; D013673:Technology Assessment, Biomedical / Q000191:economics / Q000379:methods*