Conclusion Various methods have been used at all stages of CSPBM development .
The choice between developing a classification system de novo or from an existing instrument may depend on the availability of a suitable existing measure , while the choice between a decomposed or composite approach appears to be determined primarily by the purpose for which the instrument is design ed .
Background Health state utility values ( HSUVs ) are required to calculate quality -adjusted life-years ( QALYs ) .
They are frequently derived from generic preference-based measures of health .
However , such generic measures may not capture health attributes of relevance to specific conditions .
In such cases , a condition-specific preference-based measure ( CSPBM ) may be more appropriate .
Objective This systematic review aim ed to identify all published accounts of developing CSPBMs to describe and appraise the methods used .
Comparison of methods to estimate health state utilities for ovarian cancer using quality of life data: a Gynecologic Oncology Group study.
BACKGROUND Cost-effectiveness/cost-utility analyses are increasingly needed to inform decisions about care . Algorithms have been developed using the Functional Assessment of Cancer Therapy ( FACT ) quality of life instrument to estimate utility weights for cost analyses . This study was design ed to compare these algorithms in the setting of ovarian cancer . METHODS GOG-0152 was a 550-patient r and omized phase III trial of interval cytoreduction , and GOG-0172 was a 415-patient r and omized phase III trial comparing intravenous versus intraperitoneal therapy among women with advanced ovarian cancer .  QOL data were collected via the FACT at four time points in each study . Two published mapping algorithms ( Cheung and Dobrez ) and a linear transformation method were applied to these data . The agreement between measures was assessed by the concordance correlation coefficient ( r(CCC ) ) , and paired t-tests were used to compare means . RESULTS While agreement between the estimation algorithms was good ( ranged from 0.72 to 0.81 ) , there were statistically significant ( p<0.001 ) and clinical ly meaningful differences between the scores : mean scores were higher with Dobrez than with Cheung or the linear transformation method . Scores were also statistically significantly different ( p<0.001 ) between studies . CONCLUSIONS In the absence of prospect ively collected utility data , the use of mapping algorithms is feasible , however , the optimal algorithm is not clear . There were significant differences between studies , which highlight the need for validation of these algorithms in specific setting s. If cost analyses incorporate mapping algorithms to obtain utility estimates , investigators should take the variability into account
Conclusion Various methods have been used at all stages of CSPBM development .
The choice between developing a classification system de novo or from an existing instrument may depend on the availability of a suitable existing measure , while the choice between a decomposed or composite approach appears to be determined primarily by the purpose for which the instrument is design ed .
Background Health state utility values ( HSUVs ) are required to calculate quality -adjusted life-years ( QALYs ) .
They are frequently derived from generic preference-based measures of health .
However , such generic measures may not capture health attributes of relevance to specific conditions .
In such cases , a condition-specific preference-based measure ( CSPBM ) may be more appropriate .
Objective This systematic review aim ed to identify all published accounts of developing CSPBMs to describe and appraise the methods used .