Abstract
BackgroundCost utility analysis (CUA) using SF-36/SF-12 data has been facilitated by the development of several preference-based algorithms. The purpose of this study was to illustrate how decision-making could be affected by the choice of preference-based algorithms for the SF-36 and SF-12, and provide some guidance on selecting an appropriate algorithm.MethodsTwo sets of data were used: (1) a clinical trial of adult asthma patients; and (2) a longitudinal study of post-stroke patients. Incremental costs were assumed to be $2000 per year over standard treatment, and QALY gains realized over a 1-year period. Ten published algorithms were identified, denoted by first author: Brazier (SF-36), Brazier (SF-12), Shmueli, Fryback, Lundberg, Nichol, Franks (3 algorithms), and Lawrence. Incremental cost-utility ratios (ICURs) for each algorithm, stated in dollars per quality-adjusted life year ($/QALY), were ranked and compared between datasets.ResultsIn the asthma patients, estimated ICURs ranged from Lawrence's SF-12 algorithm at $30,769/QALY (95% CI: 26,316 to 36,697) to Brazier's SF-36 algorithm at $63,492/QALY (95% CI: 48,780 to 83,333). ICURs for the stroke cohort varied slightly more dramatically. The MEPS-based algorithm by Franks et al. provided the lowest ICUR at $27,972/QALY (95% CI: 20,942 to 41,667). The Fryback and Shmueli algorithms provided ICURs that were greater than $50,000/QALY and did not have confidence intervals that overlapped with most of the other algorithms. The ICUR-based ranking of algorithms was strongly correlated between the asthma and stroke datasets (r = 0.60).ConclusionSF-36/SF-12 preference-based algorithms produced a wide range of ICURs that could potentially lead to different reimbursement decisions. Brazier's SF-36 and SF-12 algorithms have a strong methodological and theoretical basis and tended to generate relatively higher ICUR estimates, considerations that support a preference for these algorithms over the alternatives. The "second-generation" algorithms developed from scores mapped from other indirect preference-based measures tended to generate lower ICURs that would promote greater adoption of new technology. There remains a need for an SF-36/SF-12 preference-based algorithm based on the US general population that has strong theoretical and methodological foundations.
Highlights
Cost utility analysis (CUA) using Short Form 36 (SF-36)/social functioning (SF)-12 data has been facilitated by the development of several preference-based algorithms
In the asthma patients, estimated Incremental cost-utility ratios (ICURs) ranged from Lawrence's SF-12 algorithm at $30,769/quality-adjusted life years (QALYs) to Brazier's SF-36 algorithm at $63,492
Using actual health states self-assessed by patients and imputing what might be considered conservative costs for an innovative treatment, our analysis demonstrated that ICURs based on the derivation algorithms can vary dramatically
Summary
Cost utility analysis (CUA) using SF-36/SF-12 data has been facilitated by the development of several preference-based algorithms. The Panel on Cost Effectiveness in Health and Medicine recommended that community preferences for health states collected from a representative sample of the US general population should be "the most appropriate ones for use in a Reference Case analysis" for US decision makers [2] Such an approach is facilitated by indirect preference-based generic measures of health-related quality of life (HRQL) such as the Quality of Well-Being Scale [3], Health Utilities Index [4,5], and EQ-5D [6,7], as opposed to elicitation of preferences directly from patients using techniques such as the standard gamble, rating scale, and the time trade-off. Indirect preference-based HRQL measures typically generate index-based single summary scores for health states described by the instrument's classification system using an algorithm based on preferences of the community or general population
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