Peep F M Stalmeier1. 1. Nijmegen Institute for Cognition and Information, The Netherlands. stalmeier@nici.kun.nl
Abstract
BACKGROUND: Classic utility assessment uses death and perfect health as end points. Chained utility assessment uses other health states as endpoints. It has been previously noted that these 2 assessment procedures lead to different utilities. PURPOSE: The author attempts to explain these discrepancies between chained and classic assessments. METHOD: Previous data are plotted in a uniform way to facilitate comparison. Using time trade-off and paired-comparison data, the author estimates the extent to which respondents adjust their responses when end points are varied. Data were obtained in various samples: in healthy volunteers from the general public, in students, and in women at high risk for breast cancer seeking genetic counseling. RESULTS: The author obtained 741 valid data records from a total of 106 participants. The data replicate the pattern found previously. When compared to classic utilities, (1) chained utilities are smaller (larger) when the best (worst) endpoint varies and (2) the discrepancies become smaller for utilities near 0 and 1. The data reveal that there is a distinct failure to adjust responses when the end points are varied, as if the responses anchor on some master health scale. The latter finding explains the robust pattern of discrepancies. CONCLUSION: Decision analyses that use a mix of classic and chained utilities are not on firm ground. One should be wary of normative interpretations of new value assessment procedures. Alternative interpretations of the findings are discussed.
BACKGROUND: Classic utility assessment uses death and perfect health as end points. Chained utility assessment uses other health states as endpoints. It has been previously noted that these 2 assessment procedures lead to different utilities. PURPOSE: The author attempts to explain these discrepancies between chained and classic assessments. METHOD: Previous data are plotted in a uniform way to facilitate comparison. Using time trade-off and paired-comparison data, the author estimates the extent to which respondents adjust their responses when end points are varied. Data were obtained in various samples: in healthy volunteers from the general public, in students, and in women at high risk for breast cancer seeking genetic counseling. RESULTS: The author obtained 741 valid data records from a total of 106 participants. The data replicate the pattern found previously. When compared to classic utilities, (1) chained utilities are smaller (larger) when the best (worst) endpoint varies and (2) the discrepancies become smaller for utilities near 0 and 1. The data reveal that there is a distinct failure to adjust responses when the end points are varied, as if the responses anchor on some master health scale. The latter finding explains the robust pattern of discrepancies. CONCLUSION: Decision analyses that use a mix of classic and chained utilities are not on firm ground. One should be wary of normative interpretations of new value assessment procedures. Alternative interpretations of the findings are discussed.
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