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 randomly separated into construction and validation samples. 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 systematically 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 resources within cancer centers and in understanding health states preferences among cancer experienced patients before and after treatment.
Dobrez
Deborah
D
Health Policy and Administration, School of Public Health, University of Illinois at Chicago, Chicago, IL 60612, USA. ddobrez@uic.edu
Cella
David
D
Pickard
A Simon
AS
Lai
Jin-Shei
JS
Nickolov
Angel
A
D000203:Activities of Daily Living; D000293:Adolescent; D000328:Adult; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006304:Health Status; D006801:Humans; D007407:Interviews as Topic; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000188:drug therapy*; D017060:Patient Satisfaction / Q000706:statistics & numerical data; D011788:Quality of Life; D012189:Retrospective Studies; D011795:Surveys and Questionnaires; D014481:United States