title: Mapping the Functional Assessment of Cancer Therapy-general or -Colorectal to SF-6D in Chinese patients with colorectal neoplasm.
abstract: OBJECTIVE
  To map Functional Assessment of Cancer Therapy-General (FACT-G) and Functional Assessment of Cancer Therapy-Colorectal (FACT-C) subscale scores onto six-dimensional health state short form (derived from short form 36 health survey) (SF-6D) preference-based values in patients with colorectal neoplasm, with and without adjustment for clinical and demographic characteristics. These results can then be applied to studies that have used FACT-G or FACT-C to predict SF-6D utility values to inform economic evaluation.
  
  
  METHODS
  Ordinary least square regressions were estimated mapping FACT-G and FACT-C onto SF-6D by using cross-sectional data of 537 Chinese subjects with different stages of colorectal neoplasm. Mapping functions for SF-6D preference-based values were developed separately for FACT-G and FACT-C in four sequential models for addition of variables: 1) main-effect terms, 2) squared terms, 3) interaction terms, and 4) clinical and demographic variables. Predictive performance in each model was assessed by the R(2), adjusted R(2), predicted R(2), information criteria (Akaike information criteria and Bayesian information criteria), the root mean square error, the mean absolute error, and the proportions of absolute error within the threshold of 0.05 and 0.10.
  
  
  RESULTS
  Models including FACT variables and clinical and demographic variables had the best predictive performance measured by using R(2) (FACT-G: 59.98%; FACT-C: 60.43%), root mean square error (FACT-G: 0.086; FACT-C: 0.084), and mean absolute error (FACT-G: 0.065; FACT-C: 0.065). The FACT-C-based mapping function had better predictive ability than did the FACT-G-based mapping function.
  
  
  CONCLUSIONS
  Models mapping FACT-G and FACT-C onto SF-6D reached an acceptable degree of precision. Mapping from the condition-specific measure (FACT-C) had better performance than did mapping from the general cancer measure (FACT-G). These mapping functions can be applied to FACT-G or FACT-C data sets to estimate SF-6D utility values for economic evaluation of medical interventions for patients with colorectal neoplasm. Further research assessing model performance in independent data sets and non-Chinese populations are encouraged.
authors.affiliation: Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong. carlosho@hku.hk
authors.forename: Carlos K H
authors.initials: CK
authors.lastname: Wong
authors.forename: Cindy L K
authors.initials: CL
authors.lastname: Lam
authors.forename: Donna
authors.initials: D
authors.lastname: Rowen
authors.forename: Sarah M
authors.initials: SM
authors.lastname: McGhee
authors.forename: Ka-Ping
authors.initials: KP
authors.lastname: Ma
authors.forename: Wai-Lun
authors.initials: WL
authors.lastname: Law
authors.forename: Jensen T C
authors.initials: JT
authors.lastname: Poon
authors.forename: Pierre
authors.initials: P
authors.lastname: Chan
authors.forename: Dora L W
authors.initials: DL
authors.lastname: Kwong
authors.forename: Janice
authors.initials: J
authors.lastname: Tsang
mesh_terms: D000368:Aged; D002681:China / Q000208:ethnology; D015179:Colorectal Neoplasms / Q000145:classification* / Q000208:ethnology; D005260:Female; D006306:Health Surveys / Q000295:instrumentation*; D006723:Hong Kong; D006801:Humans; D008297:Male; D008875:Middle Aged; D057240:Patient Preference; D011788:Quality of Life / Q000523:psychology; D057566:Self Report