OBJECTIVES. 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. METHODS. Two 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 $2000 per year over standard treatment, and QALY gains realized over a 1-year period. Nine published algorithms were identified, denoted by first author: Brazier (SF-36), Brazier (SF-12), Shmueli, Fryback, Lundberg, Nichol, Franks (2 algorithms), and Lawrence. Incremental cost-utility ratios (ICURs) for each algorithm, stated in dollars per quality-adjusted life year ($/QALY), were ranked and compared between datasets. RESULTS. In the asthma patients, ICURs ranged from $15,267/QALY (Nichol, SF-36) to $31,746/QALY (Brazier, SF-36). ICURs for the stroke cohort varied more dramatically. The Nichol algorithm provided the lowest ICUR at $22,472/QALY. Lundberg, Shmueli and Brazier algorithms (SF-12 and SF-36) estimated ICURs that were greater than $100,000/QALY. The ICUR-based ranking of algorithms was strongly correlated between the asthma and stroke datasets (rS=0.69). CONCLUSIONS. SF-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 conservative ICUR estimates, considerations that support a preference for this algorithm over the alternatives. The “second-generation” algorithms developed from preferences 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.