Comparison of methods to estimate health state utilities for ovarian cancer using quality of life data: A Gynecologic Oncology Group study

Comparison of methods to estimate health state utilities for ovarian cancer using quality of life data: A Gynecologic Oncology Group study

Highlights

► Cost analyses of ovarian cancer treatment could be biased if estimation methods are used to measure quality-adjusted life years.
► Comparisons of several utility-estimation methods found significant differences (p < 0.001) between the utility values from these methods. ► There's a need to validate utility estimation methods before they can be recommended for cost analyses in ovarian cancer.

Abstract

Background

Cost-effectiveness/cost-utility analyses are increasingly needed to inform decisions about care. Algorithms have been developed using the Functional Assessment of Cancer Therapy (FACT) quality of life instrument to estimate utility weights for cost analyses. This study was designed to compare these algorithms in the setting of ovarian cancer.

Methods

GOG-0152 was a 550-patient randomized phase III trial of interval cytoreduction, and GOG-0172 was a 415-patient randomized phase III trial comparing intravenous versus intraperitoneal therapy among women with advanced ovarian cancer. QOL data were collected via the FACT at four time points in each study. Two published mapping algorithms (Cheung and Dobrez) and a linear transformation method were applied to these data. The agreement between measures was assessed by the concordance correlation coefficient (r_CCC), and paired t-tests were used to compare means.

Results

While agreement between the estimation algorithms was good (ranged from 0.72 to 0.81), there were statistically significant (p < 0.001) and clinically meaningful differences between the scores: mean scores were higher with Dobrez than with Cheung or the linear transformation method. Scores were also statistically significantly different (p < 0.001) between studies.

Conclusions

In the absence of prospectively collected utility data, the use of mapping algorithms is feasible, however, the optimal algorithm is not clear. There were significant differences between studies, which highlight the need for validation of these algorithms in specific settings. If cost analyses incorporate mapping algorithms to obtain utility estimates, investigators should take the variability into account.