Estimation of Patient Preference-Based Utility Weights from the Functional Assessment of Cancer Therapy—General
Article first published online: 3 MAY 2007
Value in Health
Volume 10, Issue 4, pages 266–272, July/August 2007
How to Cite
Dobrez, D., Cella, D., Pickard, A. S., Lai, J.-S. and Nickolov, A. (2007), Estimation of Patient Preference-Based Utility Weights from the Functional Assessment of Cancer Therapy—General. Value in Health, 10: 266–272. doi: 10.1111/j.1524-4733.2007.00181.x
- Issue published online: 3 MAY 2007
- Article first published online: 3 MAY 2007
- quality of life;
- time trade-off;
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 wellpredicted (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.