title: When does quality-adjusting life-years matter in cost-effectiveness analysis?
abstract: OBJECTIVE
  This paper investigates the impact of quality-of-life adjustment on cost-effectiveness analyses, by comparing ratios from published studies that have reported both incremental costs per (unadjusted) life-year and per quality-adjusted life-year for the same intervention.
  
  
  METHODS
  A systematic literature search identified 228 original cost-utility analyses published prior to 1998. Sixty-three of these analyses (173 ratio pairs) reported both cost/LY and cost/QALY ratios for the same intervention, from which we calculated medians and means, the difference between ratios (cost/LY minus cost/QALY) and between reciprocals of the ratios, and cost/LY as a percentage of the corresponding cost/QALY ratio. We also compared the ratios using rank-order correlation, and assessed the frequency with which quality-adjustment resulted in a ratio crossing the widely used cost-effectiveness thresholds of 20, 000 US dollars, 50,000 US dollars, and 100,000 US dollars/QALY or LY.
  
  
  RESULTS
  The mean ratios were 69,100 US dollars/LY and 103,100 US dollars/QALY, with corresponding medians of 24,600 US dollars/LY and 20,400 US dollars/QALY. The mean difference between ratios was approximately -34,300 US dollars (median difference: 1300 US dollars), with 60% of ratio pairs differing by 10,000 US dollars/year or less. Mean difference between reciprocals was 59 (QA)LYs per million dollars (median: 2.1). The Spearman rank-order correlation between ratio types was 0.86 (p<0.001). Quality-adjustment led to a ratio moving either above or below 50,000 US dollars/LY (or QALY) in 8% of ratio pairs, and across 100,000 US dollars in 6% of cases.
  
  
  CONCLUSIONS
  In a sizable fraction of cost-utility analyses, quality adjusting did not substantially alter the estimated cost-effectiveness of an intervention, suggesting that sensitivity analyses using ad hoc adjustments or 'off-the-shelf' utility weights may be sufficient for many analyses. The collection of preference weight data should be subjected to the same scrutiny as other data inputs to cost-effectiveness analyses, and should only be under-taken if the value of this information is likely to be greater than the cost of obtaining it.
authors.affiliation: Program on the Economic Evaluation of Medical Technology, Harvard Center for Risk Analysis, Harvard School of Public Health, Boston, Massachusetts 02115, USA. pneumann@hsps.harvard.edu
authors.forename: Richard H
authors.initials: RH
authors.lastname: Chapman
authors.forename: Marc
authors.initials: M
authors.lastname: Berger
authors.forename: Milton C
authors.initials: MC
authors.lastname: Weinstein
authors.forename: Jane C
authors.initials: JC
authors.lastname: Weeks
authors.forename: Sue
authors.initials: S
authors.lastname: Goldie
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Neumann
mesh_terms: D003362:Cost-Benefit Analysis; D006801:Humans; D019057:Quality-Adjusted Life Years; D014481:United States