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 .
Estimating a Preference-Based Index from the Clinical Outcomes in Routine Evaluation–Outcome Measure (CORE-OM)
Background . The Clinical Outcomes in Routine Evaluation– Outcome Measure ( CORE-OM ) is used to evaluate the effectiveness of psychological therapies in people with common mental disorders . The objective of this study was to estimate a preference-based index for this population using CORE-6D , a health state classification system derived from the CORE-OM consisting of a 5-item emotional component and a physical item , and to demonstrate a novel method for generating states that are not orthogonal . Methods . Rasch analysis was used to identify 11 emotional health states from CORE-6D that were frequently observed in the study population and are , thus , plausible ( in contrast , conventional statistical design might generate implausible states ) . Combined with the 3 response levels of the physical item of CORE-6D , they generate 33 plausible health states , 18 of which were selected for valuation . A valuation survey of 220 members of the public in South Yorkshire , United Kingdom , was undertaken using the time tradeoff ( TTO ) method . Regression analysis was subsequently used to predict values for all possible states described by CORE-6D . Results . A number of multivariate regression models were built to predict values for the 33 health states of CORE-6D , using the Rasch logit value of the emotional state and the response level of the physical item as independent variables . A cubic model with high predictive value ( adjusted R2 = 0.990 ) was selected to predict TTO values for all 729 CORE-6D health states . Conclusion . The CORE-6D preference-based index will enable the assessment of cost-effectiveness of interventions for people with common mental disorders using existing and prospect i ve CORE-OM data sets . The new method for generating states may be useful for other instruments with highly correlated dimensions
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 .