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 .
Estimation of patient preference-based utility weights from the functional assessment of cancer therapy - general.
OBJECTIVE The goal of this study was to estimate an algorithm to convert responses to the Functional Assessment of Cancer Therapy - General ( FACT-G ) to time trade-off ( TTO ) utilities based on utilities for current health elicited from cancer patients . METHODS Data for 1433 cancer patients were r and omly separated into construction and validation sample s. Four FACT-G questions were selected for inclusion based on correlation with Eastern Clinical Oncology Group - Performance Status ( ECOG-PS ) scores and TTO utilities . Item response theory was used to collapse response categories . Ordinary least squares regression with the constant constrained to one was used to estimate the algorithm . RESULTS The algorithm estimated mean utility for the full validation sample within three points of observed mean utility ( 0.805 vs. 0.832 , P < 0.01 ) . Mean utilities were well predicted ( mean absolute difference < 0.03 , P > 0.05 ) for most subgroups defined by ECOG-PS and Short Form-36 physical functioning scores , and responses to the FACT-G overall quality of life item . Nevertheless , the algorithm systematic ally overpredicted utilities for poorer health states . CONCLUSIONS A FACT-G-based algorithm of cancer patient utilities was developed that estimates group mean utility scores with accuracy comparable to other indirect preference-based measures of health-related quality of life . Patient-based preferences for health outcomes of cancer treatment may be useful in multiple situations , such as managing re sources within cancer centers and in underst and ing health states preferences among cancer experienced patients before and after treatment
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 .