Premium
When does quality‐adjusting life‐years matter in cost‐effectiveness analysis?
Author(s) -
Chapman Richard H.,
Berger Marc,
Weinstein Milton C.,
Weeks Jane C.,
Goldie Sue,
Neumann Peter J.
Publication year - 2004
Publication title -
health economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.55
H-Index - 109
eISSN - 1099-1050
pISSN - 1057-9230
DOI - 10.1002/hec.853
Subject(s) - quality adjusted life year , statistics , medicine , cost–utility analysis , rank correlation , mean difference , mathematics , cost effectiveness , confidence interval , demography , sociology
Purpose: 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 $20000, $50000, and $1/QALY or LY. Results: The mean ratios were $69100/LY and $103100/QALY, with corresponding medians of $24600/LY and $20400/QALY. The mean difference between ratios was approximately −$34300 (median difference: $1300), with 60% of ratio pairs differing by $10000/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 $50000/LY (or QALY) in 8% of ratio pairs, and across $1 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. Copyright © 2004 John Wiley & Sons, Ltd.